[Submitted on 24 Sep 2026]
Title:Memory-Aware Multi-Sensor Perception for Efficient and Safe Navigation in Dynamic Environments
View a PDF of the paper titled Memory-Aware Multi-Sensor Perception for Efficient and Safe Navigation in Dynamic Environments, by Jingshuo Li and 4 other authors
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Abstract: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.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2609.30495 [cs.RO]
(or arXiv:2609.30495v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.30495
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yifan Xue [view email] [v1] Thu, 24 Sep 2026 19:33:28 UTC (5,180 KB)
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