Time-Optimized Safe Navigation in Unstructured Environments through Learning Based Depth Completion (2506.14975v1)

Abstract: Quadrotors hold significant promise for several applications such as agriculture, search and rescue, and infrastructure inspection. Achieving autonomous operation requires systems to navigate safely through complex and unfamiliar environments. This level of autonomy is particularly challenging due to the complexity of such environments and the need for real-time decision making especially for platforms constrained by size, weight, and power (SWaP), which limits flight time and precludes the use of bulky sensors like Light Detection and Ranging (LiDAR) for mapping. Furthermore, computing globally optimal, collision-free paths and translating them into time-optimized, safe trajectories in real time adds significant computational complexity. To address these challenges, we present a fully onboard, real-time navigation system that relies solely on lightweight onboard sensors. Our system constructs a dense 3D map of the environment using a novel visual depth estimation approach that fuses stereo and monocular learning-based depth, yielding longer-range, denser, and less noisy depth maps than conventional stereo methods. Building on this map, we introduce a novel planning and trajectory generation framework capable of rapidly computing time-optimal global trajectories. As the map is incrementally updated with new depth information, our system continuously refines the trajectory to maintain safety and optimality. Both our planner and trajectory generator outperforms state-of-the-art methods in terms of computational efficiency and guarantee obstacle-free trajectories. We validate our system through robust autonomous flight experiments in diverse indoor and outdoor environments, demonstrating its effectiveness for safe navigation in previously unknown settings.

• The paper introduces a novel method that combines monocular and stereo depth estimation to create dense, long-range depth maps for improved navigation.
• The paper presents a trajectory planning framework that decomposes maps into convex sets and uses a modified A* algorithm with Bernstein spline trajectories to ensure safe, time-optimal paths.
• The paper demonstrates the system’s effectiveness through autonomous flights in complex, GPS-denied environments, outperforming traditional methods like MINCO in speed while achieving comparable optimality.

Time-Optimized Safe Navigation in Unstructured Environments through Learning-Based Depth Completion

The paper Time-Optimized Safe Navigation in Unstructured Environments through Learning-Based Depth Completion tackles the challenge of enabling quadrotors to navigate autonomously in complex and unknown environments. This research emerges at the intersection of robotics, perception, and trajectory planning, aiming to improve the autonomous capabilities of quadrotors for applications like agriculture, search-and-rescue, and infrastructure inspection.

Overview

The authors propose a novel real-time navigation system leveraging lightweight onboard sensors, addressing constraints imposed by the size, weight, and power of small aerial platforms. Traditional navigation systems often rely on systems such as LiDAR for precise mapping, which are unsuitable for compact quadrotors due to their payload constraints. Instead, this work integrates learning-based visual depth estimation methods to create dense 3D maps using stereo and monocular cues to overcome the sensor limitation.

Key Contributions

1. Advanced Depth Estimation: The paper develops an innovative visual perception algorithm combining monocular and stereo-based depth estimation. This technique yields depth maps that are longer-range, denser, and less noisy than conventional stereo methods. The authors detail a specific optimization process using inverse depth for accurate scaling, thereby achieving robust depth completeness without extensive retraining or large datasets.
2. Trajectory Generation Framework: Building on their depth maps, the authors lay out a trajectory planning framework capable of rapidly computing time-optimal, globally safe paths. This process involves decomposing the environment map into a graph of convex sets and solving for a subset connecting the start and goal states using modified A* algorithms. The proposed Bernstein polynomial spline-based trajectory generation guarantees avoidance of obstacles while optimizing traversal time.
3. Comparison and Validation: The proposed trajectory generation method runs faster than popular methods like MINCO, while maintaining similar path optimality and offering stronger guarantees. The experimental validation includes extensive autonomous flights in diverse environments, demonstrating the system's effectiveness in navigating and mapping unknown indoor and outdoor terrains, particularly in GPS-denied spaces.

Implications and Future Work

The implications of this research extend from practical applications in real-world autonomy for small quadrotors to theoretical advancements in perception and control. The successful integration of depth completion and real-time trajectory planning signifies improvements in deploying UAVs for various environmental applications where large cumbersome payloads are impractical.

For future directions, enhancing real-time map updates and depth algorithm adaptability in varied conditions could further streamline the computational loads. Extending perception capabilities to other sensor modalities could also diversify operational environments, enhancing robustness against sensor failures or environmental obstacles. The ongoing evolution of AI could contribute improved model architectures or learning paradigms, optimizing perception, planning, and control strategies in dynamic environments.