Efficient Trajectory Generation in 3D Environments with Multi-Level Map Construction

TL;DR

This paper introduces a robust and efficient framework for global trajectory generation in complex 3D environments for ground robots, utilizing multi-level map construction from point clouds and optimized path search.

Contribution

It presents a novel multi-level map construction method and a trajectory optimization approach that improves robustness and efficiency over existing techniques.

Findings

Demonstrates higher robustness to point cloud noise.

Enables high-quality trajectory generation.

Maintains high computational efficiency.

Abstract

We propose a robust and efficient framework to generate global trajectories for ground robots in complex 3D environments. The proposed method takes point cloud as input and efficiently constructs a multi-level map using triangular patches as the basic elements. A kinematic path search is adopted on the patches, where motion primitives on different patches combine to form the global min-time cost initial trajectory. We use a same-level expansion method to locate the nearest obstacle for each trajectory waypoint and construct an objective function with curvature, smoothness and obstacle terms for optimization. We evaluate the method on several complex 3D point cloud maps. Compared to existing methods, our method demonstrates higher robustness to point cloud noise, enabling the generation of high quality trajectory while maintaining high computational efficiency. Our code will be publicly available at https://github.com/ck-tian/MLMC-planner.