MSOT-Q: Multi-Sensor Fusion SLAM with Object Detection Tracking for Quadruped Robots

In recent years, unmanned robots, particularly quadruped robots, have attracted considerable attention owing to their remarkable mobility and payload capabilities. This paper introduces a system to enhance the autonomous perception of quadruped robots through object detection and map building. The primary goal is to improve target tracking and map construction capabilities. To enhance navigation proficiency in challenging environments, we employ a hierarchical controller integrating a proportional derivative control scheme with the model predictive control algorithm based on differential evolution. This controller is strategically crafted to navigate challenging environments effectively. The system underscores the fusion of data from various sensors for simultaneous localization and mapping applications, leading to precise map generation and accurate target tracking. Notably, the dynamic window approach algorithm is utilized to determine the optimal trajectory for target tracking, achieving a harmonious balance between traversability and target localization. Rigorous testing in a demanding simulation environment affirms the effectiveness of the proposed system in significantly improving both target tracking and map construction for the quadruped robot.