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An Open-Source LiDAR and Monocular Off-Road Autonomous Navigation Stack

Published 3 Apr 2026 in cs.RO | (2604.03096v1)

Abstract: Off-road autonomous navigation demands reliable 3D perception for robust obstacle detection in challenging unstructured terrain. While LiDAR is accurate, it is costly and power-intensive. Monocular depth estimation using foundation models offers a lightweight alternative, but its integration into outdoor navigation stacks remains underexplored. We present an open-source off-road navigation stack supporting both LiDAR and monocular 3D perception without task-specific training. For the monocular setup, we combine zero-shot depth prediction (Depth Anything V2) with metric depth rescaling using sparse SLAM measurements (VINS-Mono). Two key enhancements improve robustness: edge-masking to reduce obstacle hallucination and temporal smoothing to mitigate the impact of SLAM instability. The resulting point cloud is used to generate a robot-centric 2.5D elevation map for costmap-based planning. Evaluated in photorealistic simulations (Isaac Sim) and real-world unstructured environments, the monocular configuration matches high-resolution LiDAR performance in most scenarios, demonstrating that foundation-model-based monocular depth estimation is a viable LiDAR alternative for robust off-road navigation. By open-sourcing the navigation stack and the simulation environment, we provide a complete pipeline for off-road navigation as well as a reproducible benchmark. Code available at https://github.com/LARIAD/Offroad-Nav.

Summary

  • The paper presents a novel open-source pipeline that integrates LiDAR and zero-shot monocular depth estimation for off-road autonomous navigation.
  • The paper details a modular system combining 3D perception, ground segmentation, and path planning, achieving competitive success rates and improved trajectory efficiency.
  • The paper validates the approach with real and simulated experiments, showing comparable performance between LiDAR and monocular methods despite challenges in complex terrains.

An Open-Source LiDAR and Monocular Off-Road Autonomous Navigation Stack

Overview

The paper "An Open-Source LiDAR and Monocular Off-Road Autonomous Navigation Stack" (2604.03096) proposes a modular, open-source pipeline for off-road vehicle navigation capable of utilizing both LiDAR-based and monocular depth estimation for robust 3D perception. Notably, the monocular depth estimation leverages zero-shot foundation modelsโ€”specifically Depth Anything V2โ€”combined with metric rescaling via a sparse SLAM point cloud (VINS-Mono). The stack is designed for rapid, training-free deployment on new robotic platforms or environments and demonstrates comparable performance between high-resolution LiDAR and foundation model monocular perception in challenging off-road scenarios. The authors open-source their navigation code and simulation environments, facilitating reproducibility and benchmarking for further research. Figure 1

Figure 1

Figure 1: Autonomous navigation with the wheeled ground robot in the real and simulation off-road environment.

Reliable 3D perception is critical for autonomous navigation in unstructured, off-road environments due to the need for robust obstacle detection and terrain modeling. While LiDAR offers high-fidelity spatial information, its high cost, conspicuousness, and substantial power requirements limit widespread deployment. Stereo vision is less expensive but suffers from lower robustness, limited depth range, and sensitivity to environmental conditions.

Monocular depth estimation, powered by foundation models trained on vast datasets, is emerging as a lightweight alternative. Previous works have demonstrated zero-shot and metric monocular depth estimation in indoor settings, but their deployment in outdoor navigation pipelines remains scarce. Moreover, integration methods (depth completion, metric rescaling) have varying generalization capacities, efficiency, and susceptibility to hallucination or temporal inconsistency.

Existing open-source stacks for off-road navigation (e.g., NATURE [goodin2024nature]) typically restrict 3D perception to LiDAR, constraining adaptability. Navigation stacks for autonomous driving (e.g., Nav2 [macenski2020marathon2]) require substantial adaptation for off-road use.

Architecture and Pipeline

The navigation stack integrates three principal modules: 3D perception, ground segmentation and obstacle detection, and path planning. Either a LiDAR point cloud or a monocular camera image with sparse SLAM depth measurements are used as inputs. The modularity allows selection of the 3D perception method based on sensor availability and environmental constraints. Figure 2

Figure 2: Diagram of the navigation pipelineโ€”inputs include LiDAR point clouds or monocular images augmented with SLAM, yielding velocity commands.

3D Perception

LiDAR input requires no preprocessing; the monocular pipeline uses Depth Anything V2 for zero-shot normalized depth prediction. Metric scaling and shift are computed using sparse SLAM measurements, with an exponential moving average applied to mitigate SLAM instability. Edge-masking via a Sobel filter removes ambiguous obstacle boundaries from the depth map to reduce hallucinated obstaclesโ€”critical for actionable 3D map generation. Figure 3

Figure 3

Figure 3

Figure 3

Figure 3

Figure 3: Estimated rescaled depth maps and extracted 3D point clouds; edge filtering suppresses phantom obstacles to enhance navigability.

Ground Segmentation and Obstacle Detection

The 3D point cloud feeds a Cloth Simulation Filter (CSF) [zhang2016CSF], which segments ground from positive obstacles to generate an elevation map. A robot-centric memory buffer preserves known obstacles, ensuring robustness when obstacles leave the sensorโ€™s field of view.

Path Planning

The costmap is generated from the elevation map with height thresholds (30 cm default), supporting multi-level obstacle classification. The planning stack employs a global Aโˆ—A^* planner and Timed-Elastic-Bands (TEB) local planner [Rsmann2027teb], with classic sensor fusion (GNSS, IMU, SLAM) for robust localization.

Experimental Evaluation

Comprehensive experiments are conducted in both simulated (Isaac Sim) and real off-road environments. Simulation environments are designed with varying obstacle complexity and include traversable high grass scenarios that challenge perception algorithms. Real-world tests use a Shark Robotics Barakuda platform equipped with Ouster Dome LiDAR, ZED2i camera, IMU, GNSS, and Jetson AGX Orin. Figure 4

Figure 4

Figure 4

Figure 4

Figure 4

Figure 4

Figure 4

Figure 4

Figure 4

Figure 4: Top views from simulation experiments, visualizing goal locations and robot trajectories for different sensor configurations and difficulty levels.

Figure 5

Figure 5

Figure 5

Figure 5

Figure 5

Figure 5

Figure 5: Top view of real-world experimental area, showing LiDAR and monocular camera navigation trajectories across scenarios with increasing difficulty.

Metrics

Navigation performance is quantified using Success Rate (SR), Success weighted by Path Length (SPL), and Distance Ratio (DR), computed relative to manually-optimized reference trajectories.

Results

In simulation, monocular depth estimation (Mono-VINS (STCD only)) achieves competitive SR and SPL compared to LiDAR in most scenarios. Notably, for the 20m goal, the monocular configuration achieves up to 97% SR versus 67% and 63% for LiDAR real-params and sim-tuned, respectively. However, in the hardest environment with dense high grass, monocular performance degrades significantly (SR drops to 10% for 30m goal), attributed to depth estimation ambiguity and edge-masking limitations.

Real-world evaluation shows parity in SR and DR between LiDAR and monocular setups (both achieving 100%), though SPL is 22% lower for monocular due to less efficient trajectories and delayed obstacle avoidance. LiDAR-based perception provides smoother trajectories attributed to higher sensor-processing rates.

The ablation study highlights that edge-masking and parameter smoothing collectively increase SPL by up to 21%, demonstrating their effectiveness in mitigating hallucinated obstacles and temporal inconsistency in depth scaling.

Theoretical and Practical Implications

The results establish that foundation model-based monocular depth estimation is a viable alternative to LiDAR for off-road navigation, substantially lowering hardware and deployment barriers without task-specific training. The generalizability across environments and robotic platforms, provided by the modular stack, marks a significant advancement in practical field robotics.

Key theoretical implications include the integration of edge-masking to mitigate semantic hallucinations in depth maps and temporal smoothing of scaling/shift parameters derived from SLAM to achieve robust metric depth alignment. The approach affirms the utility of geometric postprocessing (CSF, costmap generation) in maintaining navigation reliability under monocular perception, but highlights ongoing limitations in unstructured features such as high grass.

Future Directions

Several avenues for improvement are identified:

  • Traversability classification: Explicit modules for distinguishing traversable high grass from non-traversable positive obstacles, potentially leveraging visual-language foundation models or traversability-focused learning.
  • Negative obstacle handling: Addressing perception and planning around negative obstacles (e.g., ditches) remains an unsolved challenge due to the absence of explicit observations.
  • ROS2 support and parallelism: Transition to ROS2 for maintainability and efficiency; multi-GPU deployment for depth estimation and elevation mapping to boost output frequency and responsiveness.

Further research can leverage the open-sourced pipeline and simulation environments as benchmarks for rigorous evaluation of navigation stacks, supporting fair and reproducible advances in the domain.

Conclusion

The open-source navigation stack presented demonstrates robust off-road navigation using either LiDAR or monocular foundation model-based depth estimation, without additional task-specific training. Monocular perception matches LiDAR in most scenarios, offering a lightweight alternative for unstructured environments. The stackโ€™s modularity, empirical evaluation, and public availability enable both practical deployment and future research advancement in autonomous navigation. Challenges persist in accurate traversability modeling, negative obstacle perception, and computational scalability, motivating ongoing development and adaptation for broader robotic applications.

Whiteboard

Explain it Like I'm 14

What is this paper about?

This paper shows how to make a small offโ€‘road robot drive itself around using either:

  • a laser scanner (LiDAR), or
  • a single regular camera (โ€œmonocularโ€)

The clever part is that the camera version doesnโ€™t need extra training for each new place. The authors combine a big, preโ€‘trained AI model that guesses depth from a single photo with some smart tricks so the robot can โ€œseeโ€ the 3D world well enough to avoid obstacles. They release all the code and a realistic simulator so others can try it.

What were the main goals?

The team wanted to:

  • Build a complete, readyโ€‘toโ€‘use navigation system for rough outdoor terrain.
  • Make it work with either LiDAR or just one camera, without training on new data.
  • Fix common cameraโ€‘depth problems (like โ€œfakeโ€ obstacles and jumpy measurements) so itโ€™s reliable outdoors.
  • Share the whole system and test worlds openly so people can repeat and compare results.

How does their system work?

Think of the robot as a careful hiker: it looks around, builds a mental map, and plans a safe path. The system has three main parts.

1) Seeing the 3D world (Perception)

Two options:

  • LiDAR: A device that spins and shoots laser beams, measuring how far things are. Itโ€™s very accurate but expensive and uses more power.
  • One camera: Like seeing with one eye. A large AI model called Depth Anything V2 looks at a single image and estimates how far each pixel is. This is fast to set up (no extra training), but raw results can be messy.

To make camera depth usable:

  • Metric rescaling: The AIโ€™s depth is โ€œrelative,โ€ not in real meters. The robot also runs a SLAM system (VINSโ€‘Mono), which tracks a few points in 3D as it moves. It uses these โ€œknownโ€ points to convert the AIโ€™s depth into realโ€‘world metersโ€”like using a ruler in the scene to set the scale.
  • Edge masking: Depth edges can be blurry, which can create โ€œphantomโ€ obstacles when turned into 3D. The system detects strong depth edges and ignores a thin band of pixels around them, cutting down on fake obstacles.
  • Temporal smoothing: Sometimes the scale jumps around from frame to frame. The system averages the scale over time so the depth stays steady.

Result: a clean 3D point cloud (a big set of dots in space) showing ground and obstacles.

2) Building a map of safe vs. risky areas

  • Ground vs. obstacles: They use a โ€œcloth simulation filterโ€ (CSF). Imagine flipping the world upside down and draping a tight sheet over it. Where the sheet touches is the ground; anything sticking out is an obstacle. This helps on uneven terrain.
  • Elevation map (2.5D): The robot keeps a grid around itself that stores the height of obstacles. It also remembers obstacles for a while as it moves, so they donโ€™t instantly disappear when out of view.
  • Costmap: The height map is turned into a simple โ€œcostโ€ map of whatโ€™s safe to drive on and what to avoid.

3) Planning a path and moving

  • Global path: An A* planner finds a route through the costmap.
  • Local path: A Timedโ€‘Elasticโ€‘Band (TEB) planner refines the path in real time, adjusting smoothly around obstacles.
  • Positioning: The robotโ€™s location is estimated by fusing GPS, IMU (motion sensors), and SLAM using a Kalman filterโ€”basically averaging trusted sources to stay on track.

How did they test it?

They tried both LiDAR and camera setups in:

  • Photorealistic simulations (NVIDIA Isaac Sim) with three difficulty levels:
    • Easy: flat ground, simple blocks.
    • Medium: realistic trees and rocks.
    • Hard: slopes and tall grass.
  • Real outdoor tests with a wheeled robot (Barakuda) across easy, medium, and hard courses.

They measured:

  • Success Rate: Did the robot reach the goal?
  • Path Efficiency (SPL): How close was the path length to a good reference path?
  • Distance Ratio: How far toward the goal it progressed, even if it didnโ€™t finish.

What did they find, and why is it important?

Main takeaways:

  • The cameraโ€‘only system worked nearly as well as a highโ€‘end LiDAR in most tests.
  • In real outdoor runs, both LiDAR and camera reached all goals. The camera routes were usually a bit longer and sometimes less smooth (slower processing and a narrower view can make the robot react later).
  • The camera struggled with tall, fuzzy objects like high grass in the hardest simulation. Blurry edges and fine vegetation can still confuse depth, even with edge masking.
  • Their two camera fixesโ€”edge masking and temporal smoothingโ€”improved reliability and efficiency compared to not using them.
  • The camera approach uses much less power and is far cheaper than LiDAR, which is great for small or lowโ€‘cost robots.

Why this matters:

  • Cheaper, lowerโ€‘power robots can navigate rough outdoor places (farms, parks, trails) without expensive sensors.
  • No need to retrain models for each new robot or location: it works โ€œout of the boxโ€ using a foundation model.
  • Open code and open simulations mean others can build on this, compare fairly, and improve it.

Whatโ€™s next?

The authors suggest:

  • Teaching the system to recognize โ€œsoftโ€ obstacles like tall grass as driveโ€‘through, not solid walls (traversability learning).
  • Handling โ€œnegative obstaclesโ€ (like holes or ditches) that are hard to see because theyโ€™re missing, not sticking out.
  • Porting the stack to ROS2 for longerโ€‘term support and easier adoption.

Overall, this work shows that a single camera plus smart software can be a strong standโ€‘in for LiDAR in offโ€‘road navigation, making autonomous robots more affordable and accessible. The full stack and simulator are openโ€‘source at: https://github.com/LARIAD/Offroad-Nav

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Knowledge Gaps

Knowledge gaps, limitations, and open questions

Below is a concise, actionable list of what the paper leaves missing, uncertain, or unexplored, to guide future research.

  • Robustness of metric depth rescaling to SLAM quality: How does performance degrade with sparse-depth density, outlier rate, or scale drift from different SLAM systems (e.g., VINS-Mono vs. ORB-SLAM3) under texture-poor terrain, motion blur, rolling shutter, or dynamic scenes?
  • Simulationโ€“real gap in sparse depth: In simulation, rescaling relies on Shiโ€“Tomasi features with ground-truth depth (STCD-only), not an actual SLAM; how representative is this of real-world conditions, and how does performance change with realistic simulated SLAM noise and failure modes?
  • Temporal smoothing trade-offs: The exponential moving average for scale/shift (ฮฑ=0.8) lacks sensitivity analysisโ€”what are the latency/stability trade-offs, and how should ฮฑ be adapted to scene dynamics to avoid delayed reactions or oscillations?
  • Edge-masking heuristic generality: Sobel-based edge removal with a fixed five-pixel band lacks adaptivity; how to auto-tune mask width to image resolution/FOV/depth range and reduce removal of thin, safety-relevant obstacles (e.g., poles, wires)?
  • Failure cases with vegetative obstacles: High grass causes persistent โ€œphantom obstaclesโ€ due to blurred boundaries; what perception modules (e.g., semantic traversability, depth confidence weighting, multi-scale edge detection) best distinguish soft/traversable vegetation from rigid obstacles?
  • Depth model and rescaling alternatives: No comparison across monocular depth models (e.g., UniDepth, Metric3D, Marigold-DC) or between rescaling vs. zero-shot metric depth vs. depth completion; which combination offers best off-road robustness vs. compute?
  • Uncertainty-aware mapping: The pipeline treats depth deterministically; how to incorporate depth confidence/uncertainty into elevation maps and planning to reduce risk from hallucinations and spurious points?
  • CSF parameter sensitivity and online adaptation: Ground segmentation via CSF requires environment-specific parameters; can parameters be self-tuned online, or should alternative ground filters be used that are more robust to noisy camera-derived point clouds?
  • Representation limits of 2.5D elevation maps: Overhangs, tunnels, and multi-level structures cannot be represented; how to extend to 3D voxel maps or layered elevation with minimal compute overhead on embedded hardware?
  • Negative obstacles (drop-offs, ditches): Not handled; what sensing/planning strategies (e.g., raycasting, stereo add-on, monocular cues, risk-sensitive planning) enable reliable detection and safe behavior around depressions?
  • Traversability beyond height thresholding: A fixed 30 cm height threshold ignores slope, roughness, deformability, and robot-terrain interaction; how to integrate terrain features (slope/roughness/friction) and robot-specific capabilities into the costmap?
  • Latency and control stability: The monocular pipeline reduces module frequencies (e.g., elevation mapping at ~6 Hz), causing late turns; what are acceptable end-to-end latency budgets for stable control, and which asynchronous scheduling or pipelining strategies mitigate delays?
  • Compute footprint and deployment envelope: No measurements of power, thermal throttling, or behavior on lower-power platforms; how to meet real-time constraints on smaller GPUs/CPUs, and what are the runtime/energy trade-offs?
  • Multi-GPU or heterogeneous acceleration: Authors suggest multi-GPU as a future improvement but do not implement it; what is the achievable throughput with model parallelism, DLA offloading, or network distillation/quantization?
  • Field-of-view limitations: A single forward monocular camera reduces anticipatory planning; how much do multi-camera panoramas, fisheye lenses, or camera placement/height affect success rate and SPL in cluttered off-road scenes?
  • Sensor fusion opportunities: The stack supports either LiDAR or monocular camera, not fused; can lightweight fusion (e.g., sparse LiDAR+mono depth) or radar augment robustness in vegetation, dust, fog, and glare?
  • Dynamic obstacles and motion prediction: Evaluation focuses on static scenes; how does the pipeline handle moving agents, and what modules (tracking, dynamic-costmaps, predictive TEB) are needed for safe avoidance?
  • Localization robustness in GNSS-denied settings: Real-world tests used GNSS+IMU+SLAM; how does the system perform under GNSS outages (e.g., forests), degraded IMU, or camera occlusion, and what fallback strategies exist?
  • Map staleness and memory policies: The elevation map keeps obstacles until updated; what aging/visibility reasoning prevents stale obstacles from blocking paths when the environment changes?
  • Planner sensitivity and auto-tuning: TEB and CSF parameters are hand-tuned and cause aborts in hard cases; can automatic parameter tuning or online adaptation (e.g., learning-based weights, constraint relaxation) reduce failure rates?
  • Costmap inflation and discretization effects: No analysis of grid resolution/inflation parameters on narrow-gap traversals and false blockages, especially with blurred/uncertain obstacles (e.g., high grass); what settings optimize safety vs. passability?
  • Safety layer and fail-safes: No discussion of emergency braking, watchdogs for perception dropouts, or conservative fallback behaviors when depth becomes unreliable or SLAM fails.
  • Calibration and drift: The impact of camera intrinsics/extrinsics errors and time synchronization on metric depth accuracy is not quantified; how sensitive is navigation to small calibration or timestamp offsets?
  • Benchmark breadth and statistical power: Real-world evaluation uses only 3 runs per scenario and a single robot/domain; broader datasets, varied lighting/weather (night, rain, fog, dust), and more repetitions are needed for statistically robust conclusions.
  • Standardized comparisons: Aside from internal configurations, no baseline comparisons against stereo depth, learning-based traversability stacks, or other open-source pipelines; creating side-by-side benchmarks would clarify benefits and trade-offs.
  • ROS2 migration and maintainability: The current ROS1 implementation hinders adoption; beyond porting, what architectural refactors (component lifecycles, DDS tuning) are needed to ensure determinism and high-rate operation?
  • Formal failure analysis: Abort cases (e.g., in high grass) lack quantified causes (perception vs. mapping vs. planning); a systematic attribution framework would guide targeted improvements.
  • Dataset/benchmark annotations: The released environments lack labeled traversability/obstacle ground truth for real-world runs; providing annotations would enable finer-grained evaluation (precision/recall of obstacles, depth error vs. distance, edge-mask efficacy).
  • Safety implications of edge filtering: Masking edges can suppress small or thin obstacles; what safeguards (e.g., conservative dilation, confidence thresholds) ensure that safety-critical obstacles are not removed?
  • Confidence-driven outlier rejection: The point cloud generation lacks a learned or probabilistic outlier filter; can depth confidence maps or geometric consistency checks reduce phantom obstacles without masking large regions?
  • Camera contamination and adverse conditions: No experiments under lens dirt, water droplets, glare, or low light; what robustness measures (defogging, auto-exposure control, HDR, active illumination) are required for field deployment?
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Practical Applications

Immediate Applications

Below are practical, deployable use cases that can leverage the paperโ€™s open-source stack, methods, and benchmarks today.

  • Low-cost off-road UGV autonomy without LiDAR
    • Sector: agriculture, forestry, energy utilities (solar/wind farm inspection), construction, environmental monitoring, public safety
    • What: Replace or augment LiDAR with a monocular camera for 3D perception and obstacle avoidance using the paperโ€™s Depth Anything V2 + VINS-Mono rescaling, edge masking, and temporal smoothing, feeding CSF-based elevation mapping and costmap planning (A* + TEB).
    • Benefits: Significant bill-of-materials and power savings (โ‰ˆ20W for LiDAR vs โ‰ˆ2W for cameras), lower detectability in sensitive operations.
    • Tools/workflow: ROS1 stack (Offroad-Nav), Isaac Sim scenarios for rapid validation, Jetson AGX Orin or x86+GPU deployment, on-robot tuning of CSF and TEB.
    • Assumptions/dependencies: Good cameraโ€“IMU calibration; visual feature-rich scenes for VINS-Mono; GNSS/IMU fusion available; compute headroom for Depth Anything V2 (โ‰ˆ10 Hz on Jetson Orin); parameter tuning for CSF/TEB; limited robustness to high grass and negative obstacles.
  • Retrofit of existing ground robots to reduce energy and cost
    • Sector: robotics OEMs, integrators, defense/security
    • What: Swap high-power LiDAR for monocular perception where mission risk and environment allow (e.g., patrol, perimeter inspection, stealthy reconnaissance).
    • Tools/workflow: Drop-in ROS1 node integration; side-by-side A/B testing using provided metrics (SR/SPL/DR).
    • Assumptions/dependencies: Equivalent field-of-view to maintain coverage; lighting conditions suitable for monocular vision; retraining not required but parameter tuning is.
  • Rapid prototyping and trade-off evaluation between LiDAR and vision-only stacks
    • Sector: industry R&D, academia, procurement
    • What: Use the open-source Isaac Sim environments and metrics to benchmark power, cost, and performance trade-offs when selecting sensors and compute.
    • Tools/workflow: Provided simulation assets and reference trajectories; batch evaluation across easy/medium/hard terrains and 10/20/30 m goals.
    • Assumptions/dependencies: Simulation-to-real gap; STCD-based reference-depth stand-in used in sim (note: in sim, VINS feature tracking is unreliable); care in interpreting sim-only results.
  • Education and training in off-road autonomy
    • Sector: academia, vocational programs, robotics bootcamps
    • What: Course modules/labs on off-road navigation that cover monocular depth estimation, SLAM-aided depth rescaling, ground segmentation via CSF, and local/global planning.
    • Tools/workflow: Reproducible stack and benchmark; ROS bag replays; parameter sensitivity exercises (CSF rigidity, TEB costs).
    • Assumptions/dependencies: ROS1 familiarity; GPU access for Depth Anything V2; basic GNSS/IMU for localization exercises.
  • Teleoperation assistance with on-board perception and costmaps
    • Sector: public safety, mining, construction, utilities
    • What: Use the monocular-derived elevation map and costmap to provide operator overlays (risk-aware teleop with persistent obstacle memory).
    • Tools/workflow: Run the 2.5D elevation mapping and costmap in parallel to teleop; display via RViz or custom UI; persist obstacles via map memory buffer.
    • Assumptions/dependencies: Adequate camera viewpoint; compute budget for simultaneous inference and mapping.
  • Open-source benchmarking for perception and planning components
    • Sector: software and robotics research
    • What: Swap in alternative monocular depth models, SLAM backends, ground filters, and local planners and compare fairly using the released metrics and scenarios.
    • Tools/workflow: Plug-and-play modules in ROS1; Isaac Sim maps; SR/SPL/DR reporting templates.
    • Assumptions/dependencies: Consistent calibration and coordinate frames; rigorous baselining; compute constraints comparable across variants.
  • Energy-optimized long-duration missions
    • Sector: environmental monitoring, agriculture, conservation
    • What: Extend mission time by replacing LiDAR with camera-based perception on battery-limited platforms.
    • Tools/workflow: Power budgeting with module frequencies (3D perception 10 Hz, elevation mapping 6 Hz in Mono); mission planning optimizing compute duty cycles.
    • Assumptions/dependencies: Acceptable navigation performance with reduced sensor power; daylight or assisted lighting.
  • Stealthy operations where active sensors are undesirable
    • Sector: defense, wildlife monitoring, anti-poaching
    • What: Camera-only navigation to minimize active emissions and sensor detectability.
    • Tools/workflow: Monocular pipeline with tuned edge masking to reduce hallucinated obstacles; conservative TEB inflation for safety.
    • Assumptions/dependencies: Visual conditions adequate for depth inference; constraints in dense vegetation remain.

Long-Term Applications

These opportunities require additional research, scaling, or engineering maturation.

  • Robust traversability in dense vegetation and deformable terrain
    • Sector: agriculture, forestry, planetary exploration, construction
    • What: Integrate semantic traversability modules to classify high grass vs solid obstacles, improving passability decisions and path quality.
    • Potential tools/products: Vision-language or segmentation foundation models fused with monocular depth; learned traversability cost layers; adaptive CSF parameters.
    • Assumptions/dependencies: Additional sensor training or zero-shot semantics; real-time performance on embedded hardware; robust data for edge cases.
  • Negative obstacle detection (ditches, holes, drop-offs)
    • Sector: mining, construction, defense, safety-critical robotics
    • What: Extend the stack to detect and plan around terrain depressions without explicit returns.
    • Potential tools/products: Multi-view depth fusion, temporal height consistency checks, complementary sensing (stereo, radar, lightweight depth).
    • Assumptions/dependencies: Enhanced perception beyond monocular depth; careful false-negative mitigation for safety certification.
  • ROS2, safety, and reliability-grade releases
    • Sector: industrial robotics, OEMs
    • What: Migrate to ROS2 for long-term maintainability, real-time QoS, and ecosystem adoption; pursue functional safety pathways for certain use cases.
    • Potential products: ROS2 packages, Docker images, CI-tested releases; diagnostics and failover modules; watchdogs for SLAM/perception health.
    • Assumptions/dependencies: Engineering resources for migration; certification processes; integration with standardized sensor drivers.
  • Heterogeneous compute optimization and multi-GPU scaling
    • Sector: embedded AI, autonomy platforms
    • What: Improve runtime by distributing Depth Anything V2 and elevation mapping across GPU/DLA/CPU; pipeline parallelism; model distillation.
    • Potential tools/products: TensorRT pipelines with operator placement; distilled depth models; HALO schedulers for Jetson.
    • Assumptions/dependencies: Maintained accuracy at higher FPS; hardware availability; thermal and power envelopes.
  • Sensor fusion variants for harsher conditions
    • Sector: defense, all-weather operations, autonomous maintenance
    • What: Fuse monocular depth with low-power 2D LiDAR or radar to handle adverse lighting, dust, rain, or texture-poor scenes.
    • Potential tools/products: Lightweight radar cost layers; probabilistic costmap fusion; adaptive sensor weighting based on confidence.
    • Assumptions/dependencies: Additional hardware; calibration and synchronization; fusion algorithm development.
  • Scaled deployment in multi-robot fleets
    • Sector: agriculture, utilities, environmental monitoring
    • What: Centralized monitoring and map sharing for numerous camera-only UGVs; cost-effective fleet operations over large off-road sites.
    • Potential tools/products: Fleet management software; map-merging for 2.5D elevation layers; edge-cloud offloading for heavy inference.
    • Assumptions/dependencies: Robust comms; synchronization and localization consistency; cloud security.
  • Policy and standards for vision-first off-road autonomy
    • Sector: regulators, public agencies, insurers
    • What: Define test protocols, benchmarks, and acceptance criteria for camera-only off-road robots using reproducible, open simulation suites.
    • Potential tools/products: Standardized evaluation harnesses; SR/SPL/DR adoptees; procurement templates emphasizing cost/energy trade-offs.
    • Assumptions/dependencies: Multi-stakeholder engagement; traceability and documentation; alignment with safety frameworks.
  • Cross-domain extensions: legged robots, micro-rovers, and drones near-ground operations
    • Sector: legged robotics, research, exploration
    • What: Adapt monocular depth rescaling and edge masking to locomotion planners or near-ground aerial inspection where LiDAR is impractical.
    • Potential tools/products: Interfaces to elevation mapping for foothold planners; specialized edge filters for thin structures; lighter models for NPUs.
    • Assumptions/dependencies: Different dynamics and planner requirements; control loop latency budgets; re-tuning of CSF and TEB analogs.
  • Data generation and self-supervised learning pipelines
    • Sector: autonomy R&D, academia
    • What: Use the stack to generate aligned imageโ€“depthโ€“elevation datasets for self-supervised depth/traversability research in off-road settings.
    • Potential tools/products: Logging and labeling tools; benchmark leaderboards; synthetic-to-real domain adaptation workflows.
    • Assumptions/dependencies: Data quality controls; consistent calibration; privacy/usage compliance.
  • Commercial products: camera-first off-road nav kits
    • Sector: robotics OEMs, integrators
    • What: Turn the stack into a productized moduleโ€”sensor, compute, and software bundleโ€”for small to mid-size UGVs in rugged terrain.
    • Potential tools/products: Ruggedized camera + IMU kit; pre-tuned CSF/TEB profiles by terrain class; support contracts; cloud analytics.
    • Assumptions/dependencies: Support and maintenance; clear performance envelopes (terrain/lighting); upgrade path to ROS2.

These applications hinge on key dependencies observed in the paper: the efficacy of Depth Anything V2 for zero-shot depth; reliable sparse depth from VINS-Mono; proper calibration and parameter tuning; sufficient compute (especially GPU); and acknowledged limitations around high grass, negative obstacles, and execution frequency on embedded platforms.

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Glossary

  • 2.5D elevation map: A height-coded grid representation of terrain where each cell stores a single elevation value relative to the robot; "The resulting point cloud is used to generate a robot-centric 2.5D elevation map for costmap-based planning."
  • Back-projection: The process of projecting depth image pixels into 3D space to form points; "the 3D back-projection of the depth map may result in an environment containing large phantom obstacles that may block the robot."
  • Cloth Simulation Filter (CSF): A ground-segmentation algorithm that simulates a cloth draped over an inverted point cloud to separate ground from obstacles; "a cloth simulation filter (CSF)~\cite{zhang2016CSF} is applied in order to segment the ground from the positive obstacles."
  • Costmap: A grid where cells encode traversal cost or obstacles for planning paths; "converted into a costmap used for path planning and generating the robot commands."
  • Depth Anything V2: A foundation-model-based monocular depth estimator used without task-specific training; "For the monocular setup, we combine zero-shot depth prediction (Depth Anything V2) with metric depth rescaling using sparse SLAM measurements (VINS-Mono)."
  • Depth completion (zero-shot): Inferring a dense metric depth map by fusing an image with sparse depth inputs without task-specific training; "Zero-shot depth completion methods~\cite{viola2024marigolddc, lin2025prompting} fuse an input image with sparse depth measurements to generate a metric depth map."
  • Depth rescaling: Recovering metric depth by estimating global scale and shift for normalized monocular depth predictions; "Depth rescaling techniques~\cite{marsal2025simple, guo2025monocular} estimate scaling and shift parameters to recover metric depth from normalized predictions produced by zero-shot monocular foundation models."
  • Disparity map: An image where pixel values encode relative disparity (inverse depth) used by stereo/monocular models; "We apply a Sobel filter to the disparity map returned by the depth estimation model so as to only detect edges caused by depth discontinuities, especially those due to close objects."
  • Edge-masking: Postprocessing that removes pixels near depth discontinuities to avoid false obstacles; "Two key enhancements improve robustness: edge-masking to reduce obstacle hallucination and temporal smoothing to mitigate the impact of SLAM instability."
  • Exponential moving average: A recursive smoothing technique that weights recent estimates more heavily; "we smooth these parameters with the ones obtained at previous rescaling iteration using an exponential moving average:"
  • Extended Kalman filter (EKF): A nonlinear state estimation method that fuses multiple sensors for localization; "The localization is given by a classical data fusion through an extended Kalman filter based on GNSS, IMU data and SLAM pose estimates."
  • FP16 quantization: Using 16โ€‘bit floating-point precision to speed up and reduce memory for neural network inference; "the normalized depth map is estimated using Depth Anything V2 \cite{yang2024DAV2} accelerated through FP16 quantization and TensorRT optimization."
  • Foundation model: A large, pre-trained model used zero-shot for downstream tasks without additional training; "Monocular depth estimation using foundation models offers a lightweight alternative, but its integration into outdoor navigation stacks remains underexplored."
  • GNSS: Global Navigation Satellite System, providing absolute positioning for the robot; "The localization is given by a classical data fusion through an extended Kalman filter based on GNSS, IMU data and SLAM pose estimates."
  • IMU: Inertial Measurement Unit, providing acceleration and rotation measurements for state estimation; "The localization is given by a classical data fusion through an extended Kalman filter based on GNSS, IMU data and SLAM pose estimates."
  • Isaac Sim: NVIDIAโ€™s photorealistic robotics simulation environment used for evaluation; "Evaluated in photorealistic simulations (Isaac Sim) and real-world unstructured environments"
  • LiDAR: A laser-based 3D ranging sensor producing accurate point clouds; "Most approaches rely on LiDAR for this task due to its high accuracy."
  • Metric depth: Depth values expressed in real-world units (e.g., meters); "Zero-shot monocular metric depth estimation~\cite{piccinelli2024unidepth, hu2024metric3d} consists in inferring a metric depth map directly from a single camera image."
  • Monocular depth estimation: Predicting scene depth from a single RGB image; "Monocular depth estimation using foundation models offers a lightweight alternative, but its integration into outdoor navigation stacks remains underexplored."
  • Nav2 stack: A ROS2 navigation framework for path planning and control that can be adapted to off-road use; "It is worth noting that a more generic navigation stack such as the Nav2 stack \cite{macenski2020marathon2} can also be leveraged but it must be adapted for off-road navigation."
  • Negative obstacles: Depressions or holes in terrain that appear as missing data and are difficult to detect; "Then, we plan to handle negative obstacles that are a significant challenge due to the absence of information."
  • Obstacle hallucination: Falsely detected obstacles caused by perception errors, e.g., at object edges; "Two key enhancements improve robustness: edge-masking to reduce obstacle hallucination and temporal smoothing to mitigate the impact of SLAM instability."
  • Point cloud: A set of 3D points representing the environment acquired via sensors or reconstruction; "Most off-road autonomous navigation approaches perform 3D perception using a LiDAR \cite{elnoor2024amco, ruetz2024foresttrav, frey2024roadrunner} to generate an accurate point cloud of the surrounding environment."
  • Robot-centric (map): A map maintained in the robotโ€™s local coordinate frame and updated as the robot moves; "The height of the obstacles, corresponding to the distance between the points and the cloth surface, is stored in a robot-centric $2.5$D grid map, the elevation map."
  • ROS Noetic: A ROS1 distribution used as the middleware framework for the stack; "Our navigation stack has been implemented with the ROS Noetic framework."
  • Shi-Tomasi Corner Detector (STCD): A feature detector used to select salient image points for tracking or depth sampling; "we sample points in the camera image with the Shi-Tomasi Corner Detector (STCD) \cite{shi1994good} used by VINS-Mono and leverage the ground-truth depth at these locations as reference in the rescaling module."
  • SLAM: Simultaneous Localization and Mapping, estimating robot pose while building a map; "the 3D perception module takes as input either a LiDAR point cloud or an image from a monocular camera and the sparse point clouds returned by a SLAM."
  • Temporal smoothing: Filtering estimates over time to reduce fluctuations from sensor/algorithm instability; "Two key enhancements improve robustness: edge-masking to reduce obstacle hallucination and temporal smoothing to mitigate the impact of SLAM instability."
  • TensorRT: NVIDIAโ€™s inference optimizer/runtime used to accelerate deep models on GPUs; "the normalized depth map is estimated using Depth Anything V2 \cite{yang2024DAV2} accelerated through FP16 quantization and TensorRT optimization."
  • Timed-Elastic-Bands (TEB) local planner: A trajectory optimization-based local planner that deforms elastic bands to produce feasible paths; "It provides the high-level path to follow for the Timed-Elastic-Bands (TEB) local planner \cite{Rsmann2027teb} which computes a flexible path planning for better robustness to perception errors."
  • Traversability: The property of terrain being safely passable by a robot; "A first improvement step would be to integrate traversability modules capable of classifying high grass as traversable."
  • VINS-Mono: A monocular visual-inertial SLAM system providing sparse metric structure and pose; "The reference point cloud used for rescaling is given by VINS-Mono \cite{qin2018vins} and the temporal smoothing parameter is set to ฮฑ=0.8\alpha=0.8."
  • Visual-inertial SLAM: SLAM that fuses camera and IMU data to estimate motion and sparse structure; "The sparse depth used for estimating the scaling and shift parameters is given by a visual-inertial SLAM approach (see \cref{sec:implementation} for details on the algorithm used)."
  • Zero-shot (learning): Applying a pre-trained model to new tasks or domains without task-specific training data; "Numerous zero-shot learning-based monocular depth estimation models have been developed thanks to extensive training on huge datasets."
  • Zero-shot monocular metric depth estimation: Directly predicting metric depth from a single image without additional training on the target domain; "Zero-shot monocular metric depth estimation~\cite{piccinelli2024unidepth, hu2024metric3d} consists in inferring a metric depth map directly from a single camera image."
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