動的環境における効率的で安全なナビゲーションのための記憶認識型マルチセンサー知覚
Memory-Aware Multi-Sensor Perception for Efficient and Safe Navigation in Dynamic Environments
LiDARとRGBの知覚、オンライン距離場表現学習、段階適応型制御バリア関数QPを統合し、過去に観測した構造を記憶して動的障害物を追跡しながら安全にナビゲーションするフレームワークを提案した。
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著者: Jingshuo Li, Yifan Xue, Yifei Li, Shubhodeep Shiv Aditya, Nadia Figueroa
分類: cs.RO
原文アブストラクト
Autonomous navigation in previously unseen environments requires effective perception, persistent environmental representation, and collision avoidance while maintaining progress toward a goal. Existing perception-based methods often rely on prior maps or short-horizon observations, limiting their ability to exploit previously observed structure. We propose a memory-aware multi-sensor navigation framework that integrates LiDAR and RGB perception, online distance-field representation learning, and a stage-adaptive Modulated Control Barrier Function Quadratic Program (MCBF-QP). The framework persistently represents static infrastructure while tracking dynamic obstacles, enabling the MCBF-QP controller to exploit previously observed geometry for obstacle circumvention and adapt its safety constraints and guidance to local conditions. Experiments in complex indoor and outdoor environments demonstrate improved navigation efficiency and goal-reaching performance while maintaining collision avoidance in narrow passages and around dynamic obstacles.