Towards Infrastructure-Free Autonomous Robot Navigation: Machine Learning-Based Stereo Vision vs. Motion Capture

This research examines the effectiveness of machine learning-based stereo vision as an alternative to traditional motion capture (MoCap) systems for autonomous mobile robot (AMR) navigation. Using the Unitree Go1 quadruped robot, navigation accuracy and efficiency are assessed in environments with varying obstacle densities. MoCap provides highly precise localization data, serving as a baseline for comparison, while the stereo vision system employs depth cameras to classify obstacles and generate real-time path adjustments. The stereo vision pipeline integrates disparity map computation, depth estimation, and obstacle detection to construct a dynamic 3D representation of the environment. By analyzing factors such as navigation precision, computational efficiency, and adaptability to dynamic environments, this study evaluates the feasibility of stereo vision for autonomous navigation without reliance on external tracking infrastructure, as required in MoCap. Preliminary findings indicate that stereo vision-based navigation can successfully handle complex, unstructured environments, demonstrating its potential for broader applications in robotics. The project contributes to advancements in vision- based navigation and pathfinding technologies, offering a scalable and infrastructure-free alternative for autonomous systems operating in real-world conditions.