Perception-Based Landing

Updated 17 December 2025

Perception-based landing is defined as an autonomous approach where onboard sensors replace external navigation for safe landings.
Methodologies combine stochastic control, deep learning contracts, and MPC to generate precise, safety-guaranteed trajectories in real-time.
Experimental results across UAV platforms demonstrate robust sensor fusion, adaptive control, and high landing success under diverse conditions.

Perception-based landing is the class of autonomous landing methods where sensor-derived environmental perception—rather than external navigation infrastructure (such as GNSS or ILS)—is used to guide an aerial system to a safe touchdown. This paradigm is implemented in diverse platforms, including quadcopters, fixed-wing UAVs, VTOLs, planetary rotorcraft, and piloted commercial aircraft. Core principles encompass real-time environmental sensing, online reasoning about landing safety, and closed-loop integration with flight control primitives; these elements are unified by the direct influence of perception module outputs on landing decisions and safety guarantees.

1. Mathematical Foundations and Problem Formulation

Perception-based landing is modeled as a stochastic control problem with high-dimensional state and observation spaces, typically formalized as follows:

The true system state $x\in\mathbb{R}^n$ includes vehicle pose, velocities, and possibly environmental variables.
The perception module $p:\mathbb{R}^n\to\mathbb{R}^m$ generates a noisy estimate $\hat y=p(x)$ of an external or semantic quantity $y$ (e.g., landing site position, safety mask).
The objective is to design a perception-to-control pipeline, ensuring with high probability that landing occurs within a region satisfying predefined safety criteria, even in the presence of bounded perception errors.

Advanced approaches embed these stochastic uncertainties directly into the landing controller through contracts or probabilistic sets. For instance, learning-based inverse perception contracts (IPCs) construct a contract set $\Delta(\hat y)$ for each perception output with formal guarantees: $\Pr_{x\sim D}\bigl[y\in\Delta(\hat y)\bigr] \geq 1-\delta$ where $D$ models real-world variations and $\delta$ sets the desired confidence (Sun et al., 2023).

Model Predictive Control (MPC), constrained by semantic or geometric perception information, is used to generate input trajectories respecting process, safety, and perception-derived landing constraints (Cai et al., 9 May 2025).

2. Perception Modalities and Sensing Architectures

A range of sensing and perception algorithms have been deployed:

Monocular and Multi-Modal Cameras: Visual landing pads, circle or AprilTag-style fiducials, and scene context are detected by conventional or deep learning-based detectors (e.g., SIFT/RANSAC, YOLOv8) (Saavedra-Ruiz et al., 2021, Rasul et al., 10 Dec 2025, Springer et al., 6 Mar 2024, Tasnim et al., 16 Dec 2025). Advanced methods integrate semantic segmentation and foundation models for robust region classification (Qian et al., 25 Oct 2025, Tan et al., 17 Jun 2025).
Active Sensing (Infrared/IR): Photodiode arrays localize IR beacons for precise and low-power landing, enabling operation in low-light or NLOS conditions (Liu et al., 8 Jul 2024). Infrared beacon arrays, in conjunction with narrow-band visual filtering and IMU/barometric data, support precision approach for fixed-wing vehicles (Khithov et al., 2017).
LiDAR and 3D Point Clouds: Onboard LiDAR sensors enable direct 3D hazard mapping for safe landing zone (SLZ) extraction, often leveraging multi-resolution or risk-aware Bayesian classifiers (Hayner et al., 2023, Schoppmann et al., 2021).

Hybrid sensor suites incorporating gimbal-mounted, multi-payload cameras, and thermal or zoom modalities further enhance robustness by adaptive sensor switching during the descent phase (Springer et al., 6 Mar 2024).

3. Perception-to-Decision and Safety Integration

Landing decisions rely on both geometric and semantic abstraction of perception outputs:

Geometric Contracts and Uncertainty Sets: Learned IPCs or data-driven region sets encapsulate perception error, allowing safe set-point generation (e.g., via ellipsoidal confidence regions for visual pad localization (Sun et al., 2023)).
Semantic Scene Graphs and Symbolic Reasoning: Neuro-symbolic frameworks transform pixelwise segmentation into probabilistic semantic scene graphs, leveraging rule-based logic (e.g., Datalog/Scallop programs) for verifiable, explainable landing site selection (Qian et al., 25 Oct 2025). Regions of Interest (ROIs) are scored and filtered by safety and mission utility metrics.
Reinforcement Learning and Active Perception: RL agents, trained on pixel features or geometric cues, learn policies for continuous control based on real or simulated feedback, handling dynamic targets and unstructured environments (Houichime et al., 11 May 2025, Mateus et al., 2022).
Data Augmentation and Robust Learning: Bayesian optimization of augmentation parameters systematically improves DNN robustness across lighting and weather by tuning the perception module to maximize landing success under diverse conditions (Rasul et al., 10 Dec 2024).
Latent Hazard Contracting and Reachability Analysis: Formal perception contracts (including DaRePC) specify environment and state regimes under which closed-loop safety properties provably hold, integrating sampling-based contract learning with symbolic reachability (Li et al., 2023).

4. Control Strategies and Closed-Loop Implementation

Controllers informed by perception modules operate at varying levels of abstraction:

Hybrid State Machines: Three-mode and finite-state policies implement discrete logic to sequentially measure, reposition, and commit to landing, with transitions conditioned on geometric contract regions (Sun et al., 2023).
Image-Based Visual Servoing (IBVS): Error between perceived landing pad features and ideal setpoints is mapped through proportional, PD, or PID laws to lateral and vertical velocity commands (Saavedra-Ruiz et al., 2021, Tasnim et al., 16 Dec 2025).
Advanced MPC Formulations: Perception constraints, unsafe region estimations, and semantic flags are incorporated into nonlinear MPC formulations, relaying semantic knowledge into actionable avoidance corridors and buffer enforcement (Cai et al., 9 May 2025).
Dynamic Capability Confirmation: Perception and control “Simplex” frameworks dynamically confirm vehicle braking performance, allowing higher landing speeds under guaranteed verifiable perception reliability (Bansal et al., 2023).
Trajectory Optimization with Deferred Decisions: Fuel-optimal, branched descent plans continually re-optimize over multiple candidate SLZs as perception updates site viability, supporting late “commit” maneuvers for contingency handling (Hayner et al., 2023).

5. Experimental Results and Performance Metrics

Numerous experiments validate perception-based landing systems across platforms:

Approach	Platform/Hardware	Success Rate / Error	Environment	Ref.
Learning IPC	DJI F450 + RPi3	10/10 safe (0.1m box)	Indoor, diverse pad positions	(Sun et al., 2023)
RL Lenticular Pad	DJI quadrotor (sim/hw)	7cm centering (2/3 RWT)	Monocular, static/dynamic pads	(Houichime et al., 11 May 2025)
Dual-Expert YOLO	CARLA+GUAM sim	2.53m ± 1.03m	Photorealistic, GPS-denied	(Tasnim et al., 16 Dec 2025)
IR+PD Array	Microdrone + IR beacon	9.2cm @ 11.1m range	NLOS/low light	(Liu et al., 8 Jul 2024)
Symbolic/Graph	Jetson Orin Nano	MOD 112.8px, TCD 126.8	15 datasets (covariate shifts)	(Qian et al., 25 Oct 2025)
MPC+VLE+LLM	ROS-Gazebo sim	94–96% (various env.)	Urban, grass, open, dynamic obst.	(Cai et al., 9 May 2025)
Bayesian Augment	YOLOv8 + CARLA	70%(+20pp) optimized	Day/night/rain scenarios	(Rasul et al., 10 Dec 2024)
LIDAR–HALO	Mars-like sim (AirSim)	85.6%(+18.2pp baseline)	Martian/UAV, real-time multi-candidate	(Hayner et al., 2023)

Definitions: MOD (Min Obstacle Distance), TCD (Touchdown Center Distance), RWT (real-world trial), NLOS (non-line-of-sight).

6. Domain-Specific Extensions and Limitations

Perception-based approaches address various domains, each with unique system constraints and operational challenges:

Energy and Mass Constraints: IR-based and vision-lite sensing architectures enable deployment on low-mass platforms such as microdrones, where high-power cameras or complex computation are infeasible (Liu et al., 8 Jul 2024).
Crosswind and Weather Resilience: Vision-language modeling, scene “distillation” via diffusion/Pix2Pix, and spatial transformer networks provide robustness to weather-induced visual degradations and crosswind-induced projective distortions (Pal et al., 9 May 2024).
Planetary and GPS-denied Environments: Multi-resolution mapping fuses monocular/SfM and visual-inertial odometry for SLZ detection in GPS-denied, unstructured or planetary terrain (Schoppmann et al., 2021, Rasul et al., 10 Dec 2025).
Scalable and Explainable Reasoning: Neuro-symbolic architectures facilitate transparent, formally checkable landing decisions across mission types and covariate shifts (Qian et al., 25 Oct 2025).

Key limitations include the need for copious i.i.d. training data for DNN-based contracts, difficulty generalizing to novel environments or lighting, and bottlenecks in scene graph post-processing. Extensions propose online adaptation, multi-modal sensor fusion, and jointly optimized perception-control learning.

7. Future Directions and Open Challenges

Active research pursues several advancements:

Online Learning and Adaptation: Integration of active- or uncertainty-driven data collection, online adaptation of perception contracts, and continual scene-graph enrichment aim to increase generalization and reduce sample complexity (Sun et al., 2023, Qian et al., 25 Oct 2025).
Multi-Expert and Modality Mixture-of-Experts (MoE): Expansion to additional perception experts (scale, domain, or modality-specialized) with dynamic gating and RL-based expert selection promises enhanced resilience across degradation regimes (Tasnim et al., 16 Dec 2025).
Formal Verification: Increasingly, frameworks fuse data-driven perception with symbolic contracts and reachability, formalizing closed-loop safety for rigorous certification (Li et al., 2023, Bansal et al., 2023).
End-to-End Planning–Perception Tight Coupling: Tighter perception–control and trajectory planning loops leveraging live perception updates, deferred decision making, and multi-branch replanning are maturing, especially for high-contingency and dynamic landing environments (Hayner et al., 2023).

Perception-based landing thus synthesizes contemporary advances in learning, symbolic reasoning, robust estimation, and optimal control to address the foundational challenge of autonomous, safe descent under real-world uncertainty. The field continues to evolve rapidly as demonstrated by recent work in cross-domain robustness, real-time multi-expert integration, and formal safety guarantees.