Path Planning on Multi-Level Point Cloud With a Weighted Traversability Graph

Abstract

This article proposes a new path planning method for addressing multi-level terrain situations. The proposed method includes innovations in the following three aspects: (1) the pre-processing of point cloud maps with a multi-level skip-list structure and data-slimming algorithm for well-organised and simplified map formalisation 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. Then a modified A* algorithm is employed to exploit this traversability graph and generate short and safe paths across level transitions such as ramps, bridges and overpasses. The proposed map structure reduces memory usage by over 70% and achieves a 2$\times$ speedup in surface retrieval compared to state-of-the-art approaches designed for multi-layered environments. The planner attains a 100% success rate across all evaluated scenarios, including challenging multi-level environments where conventional single-height 2.5-dimensional (2.5D) planners fail. 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 three-dimensional (3D) path planning problems for ground vehicles in a wide range of situations.