面向动态环境中高效安全导航的记忆感知多传感器感知方法
Memory-Aware Multi-Sensor Perception for Efficient and Safe Navigation in Dynamic Environments
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中文总结 AI 辅助
提出一种记忆感知多传感器导航框架，集成LiDAR和RGB感知、在线距离场学习及MCBF-QP控制器，利用历史几何信息提升动态环境中导航效率和安全性。
中文摘要 AI 辅助
在未见过的环境中进行自主导航需要有效的感知、持久的环境表示以及在朝目标前进的同时避免碰撞。现有的基于感知的方法往往依赖先验地图或短视距观测，限制了其利用先前观察到的结构的能力。我们提出了一种记忆感知的多传感器导航框架，该框架集成了LiDAR和RGB感知、在线距离场表示学习以及阶段自适应的调制控制屏障函数二次规划（MCBF-QP）。该框架持久地表示静态基础设施，同时跟踪动态障碍物，使MCBF-QP控制器能够利用先前观察到的几何信息进行障碍物规避，并根据局部条件调整其安全约束和引导。在复杂室内和室外环境中的实验表明，该方法在保持窄通道和动态障碍物周围避碰的同时，提高了导航效率和到达目标的表现。
英文摘要
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.
发表机构
- University of Pennsylvania（宾夕法尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。
