Comparison of Path Planning Algorithms for Autonomous Vehicle Navigation Using Satellite and Airborne LiDAR Data

TL;DR

This study compares various path planning algorithms for autonomous vehicles navigating complex terrains using satellite and airborne LiDAR data, highlighting Dijkstra's superior stability and efficiency.

Contribution

It provides a comprehensive evaluation of multiple algorithms on high-resolution geospatial data, introducing a novel improved ant colony optimization algorithm (NIACO) for 2D and 3D path planning.

Findings

Dijkstra outperforms other algorithms in stability and efficiency.

NIACO shows promising results as a new approach.

Algorithms are evaluated on real satellite and LiDAR datasets.

Abstract

Autonomous vehicle navigation in unstructured environments, such as forests and mountainous regions, presents significant challenges due to irregular terrain and complex road conditions. This work provides a comparative evaluation of mainstream and well-established path planning algorithms applied to weighted pixel-level road networks derived from high-resolution satellite imagery and airborne LiDAR data. For 2D road-map navigation, where the weights reflect road conditions and terrain difficulty, A*, Dijkstra, RRT*, and a Novel Improved Ant Colony Optimization Algorithm (NIACO) are tested on the DeepGlobe satellite dataset. For 3D road-map path planning, 3D A*, 3D Dijkstra, RRT-Connect, and NIACO are evaluated using the Hamilton airborne LiDAR dataset, which provides detailed elevation information. All algorithms are assessed under identical start and end point conditions, focusing on path cost, computation time, and memory consumption. Results demonstrate that Dijkstra consistently offers the most stable and efficient performance in both 2D and 3D scenarios, particularly when operating on dense, pixel-level geospatial road-maps. These findings highlight the reliability of Dijkstra-based planning for static terrain navigation and establish a foundation for future research on dynamic path planning under complex environmental constraints.