ガウス過程に基づく不整地の走行可能性解析と地図なしナビゲーション
Gaussian Process-based Traversability Analysis for Terrain Mapless Navigation
スパースガウス過程で局所地図を作り、RRT*プランナと組み合わせて不整地を安全に走行する地図なしナビゲーション手法を提案した。
著者: Abe Leininger, Mahmoud Ali, Hassan Jardali, Lantao Liu
分類: cs.RO
原文アブストラクト
Efficient navigation through uneven terrain remains a challenging endeavor for autonomous robots. We propose a new geometric-based uneven terrain mapless navigation framework combining a Sparse Gaussian Process (SGP) local map with a Rapidly-Exploring Random Tree* (RRT*) planner. Our approach begins with the generation of a high-resolution SGP local map, providing an interpolated representation of the robot's immediate environment. This map captures crucial environmental variations, including height, uncertainties, and slope characteristics. Subsequently, we construct a traversability map based on the SGP representation to guide our planning process. The RRT* planner efficiently generates real-time navigation paths, avoiding untraversable terrain in pursuit of the goal. This combination of SGP-based terrain interpretation and RRT* planning enables ground robots to safely navigate environments with varying elevations and steep obstacles. We evaluate the performance of our proposed approach through robust simulation testing, highlighting its effectiveness in achieving safe and efficient navigation compared to existing methods.
