LF-3PM：LiDARを用いた知覚認識型プランニングのための摂動誘起メトリックに基づくフレームワーク
LF-3PM: a LiDAR-based Framework for Perception-aware Planning with Perturbation-induced Metric
LiDARの観測が自己位置推定精度に与える影響を摂動誘起メトリックで評価し、静的観測損失マップを介して軌道生成を効率化する知覚認識型プランニングフレームワークを提案した。
著者: Kaixin Chai, Long Xu, Qianhao Wang, Chao Xu, Peng Yin, Fei Gao
分類: cs.RO
原文アブストラクト
Just as humans can become disoriented in featureless deserts or thick fogs, not all environments are conducive to the Localization Accuracy and Stability (LAS) of autonomous robots. This paper introduces an efficient framework designed to enhance LiDAR-based LAS through strategic trajectory generation, known as Perception-aware Planning. Unlike vision-based frameworks, the LiDAR-based requires different considerations due to unique sensor attributes. Our approach focuses on two main aspects: firstly, assessing the impact of LiDAR observations on LAS. We introduce a perturbation-induced metric to provide a comprehensive and reliable evaluation of LiDAR observations. Secondly, we aim to improve motion planning efficiency. By creating a Static Observation Loss Map (SOLM) as an intermediary, we logically separate the time-intensive evaluation and motion planning phases, significantly boosting the planning process. In the experimental section, we demonstrate the effectiveness of the proposed metrics across various scenes and the feature of trajectories guided by different metrics. Ultimately, our framework is tested in a real-world scenario, enabling the robot to actively choose topologies and orientations preferable for localization. The source code is accessible at https://github.com/ZJU-FAST-Lab/LF-3PM.
