Path Planning on Multi-level Point Cloud with a Weighted Traversability Graph
🤖 AI Summary
Path planning for ground vehicles in complex 3D terrains—such as multi-story buildings, forests, and rugged mountainous regions—remains challenging due to high geometric complexity and dynamic traversability constraints.
Method: This paper proposes a weighted traversability graph–based 3D path planning framework. It processes raw LiDAR point clouds without surface reconstruction, directly estimating local traversability via vehicle–point cloud geometric interaction analysis. A multilevel skip-list structure enables efficient point cloud preprocessing and downsampling, while a layered, connectivity-aware weighted graph integrates terrain accessibility and safety constraints.
Contribution/Results: Evaluated across diverse indoor–outdoor multi-level scenarios, the method significantly improves both path safety and computational efficiency. It generates feasible paths that jointly optimize Euclidean distance and robustness against terrain uncertainty. The approach establishes a novel paradigm for navigation of ground unmanned platforms in unstructured 3D environments.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Path planning for multi-level terrain using weighted traversability graph
Efficient point cloud pre-processing with skip-list and data-slimming
Modified A* algorithm for safe 3D path planning in diverse environments
Innovation
Methods, ideas, or system contributions that make the work stand out.
Multi-level skip-list structure for point cloud pre-processing
Direct traversability index via vehicle-cloud interaction
Weighted traversability graph for A* path planning
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