Path Planning on Multi-level Point Cloud with a Weighted Traversability Graph (2504.21622v1)

Abstract: This article proposes a new path planning method for addressing multi-level terrain situations. The proposed method includes innovations in three aspects: 1) the pre-processing of point cloud maps with a multi-level skip-list structure and data-slimming algorithm for well-organized and simplified map formalization and management, 2) the direct acquisition of local traversability indexes through vehicle and point cloud interaction analysis, which saves work in surface fitting, and 3) the assignment of traversability indexes on a multi-level connectivity graph to generate a weighted traversability graph for generally search-based path planning. The A* algorithm is modified to utilize the traversability graph to generate a short and safe path. The effectiveness and reliability of the proposed method are verified through indoor and outdoor experiments conducted in various environments, including multi-floor buildings, woodland, and rugged mountainous regions. The results demonstrate that the proposed method can properly address 3D path planning problems for ground vehicles in a wide range of situations.

Path Planning on Multi-level Point Cloud with a Weighted Traversability Graph

This paper advances the state of path planning for Unmanned Ground Vehicles (UGVs) navigating complex terrains by introducing a novel method leveraging multi-level point cloud data. The authors address key challenges associated with multi-layered environments by proposing a weighted traversability graph (WTG) that integrates terrain geometric connectivity and traversability information.

Methodological Innovations

The researchers introduced several notable methodological innovations:

1. Multi-level SkiMap Structure: The paper applies the SkipList storage structure to efficiently manage point cloud data, allowing for dynamic map resolution based on terrain complexity. This structure significantly reduces computational overhead compared to conventional octrees by storing only occupied voxels, facilitating efficient data management.
2. Weighted Traversability Graph (WTG): By assigning traversability indices derived from stereo camera data directly onto a multi-level connectivity graph, the paper constructs WTGs for ground path planning. This approach allows the A* algorithm to utilize these graphs to calculate optimal and safe paths.
3. Vehicle-Terrain Interaction Analysis: The traversability indices are computed considering vehicle dynamics and its interaction with the terrain, incorporating static analysis to account for tip-over risks, slip hazards, and potential chassis collisions.

Experimental Validation

The proposed path planning methodology was validated across diverse terrains, including indoor environments, mottles, groves, and using online datasets such as planetary caves and multi-floor buildings. These experiments demonstrate that the method successfully navigates complex scenes with obstacles, occlusions, and varying surface topologies.

Implications and Future Work

This research offers substantial practical implications for autonomous navigation in heterogeneous terrains. By optimizing path feasibility and computational efficiency in multi-level environments, this approach enhances UGV application potential across various fields, including urban navigation, agriculture, defense, and planetary exploration.

Future work could explore expanding the WTG to integrate dynamic environmental factors, incorporating real-time adaptive path planning capabilities, and extending Terrain-Terrain interaction modeling for more robust obstacle negotiation strategies.

Conclusion

The paper delivers a comprehensive and structured approach to 3D path planning, leveraging multi-layer connectivity and traversability graphs for efficient and safe UGV navigation in intricate environments. While the method streamlines computations and improves reliability in surface traversal, further research could refine its adaptability to dynamically changing terrains and broader applications in AI-driven navigation systems.