A Bio-Inspired Hybrid Slam Algorithm for Minimal-Sensor Autonomous Robotics

This paper presents an insect and mammal inspired paradigm of Simultaneous Localization and Mapping (SLAM) algorithm for minimal power robotics which preserves sensor autonomy features. It combines visual odometry with lightweight LiDar, mapping, proprioceptive, and volumetric inputs in a modular structure that optimally utilizes processing resources without loss in fidelity. We demonstrated one of the strategies by coupling a graph-based SLAM with an insect-like visual homing behavior so that it could function in harsh contexts where GPS or sensor access was denied. Also attention based models are incorporated for feature selection aiding data association to enhance reliability for loop closure. The model integrates metrical and topological mapping dynamically switching based on environmental complexity. This provides guarantee for well terrain agnostic performance throughout different landscapes. Relative to other minimal sensory SLAM systems, we achieved outstanding localization accuracy, enhanced map consistency, and verification in both simulations and on a micro robotic platform. The proposed algorithms shift autonomous energy efficiency, advantageous for small graspable robots operating in congested or confined spatial regions. The goal is to advance the research and development of autonomous robots with low bioinspired power features and realistic robust navigation systems.