Enhanced Visual SLAM and Path Planning for Autonomous Navigation of Wheeled Mobile Robots

This research focuses on important technologies used in wheeled mobile robot autonomous navigation, such as path planning optimization, system integration, and advancements in visual simultaneous localization and mapping (SLAM) techniques. An enhanced approach is suggested to overcome localization issues in traditional visual odometry brought on by duplicated or unevenly distributed feature points. This approach combines Efficient Perspective-n-Point (EPNP) feature matching, iterative closest point (ICP) pose optimization, and quadtree-based feature management. According to experimental findings, the suggested method greatly increases localization accuracy and stability. A dense point cloud reconstruction technique based on RGB-D data is developed to improve the completeness and detail of environmental representation while mitigating the sparsity often seen in point cloud maps produced by conventional SLAM systems. In order to enhance path quality and computational efficiency, an enhanced rapidly-exploring random tree (RRT) method is presented, which incorporates adaptive step-size management, goal biasing, and B-spline-based path smoothing. Furthermore, real-time local obstacle avoidance in dynamic situations is made possible by the integration of the Timed Elastic Band (TEB) algorithm. Comprehensive real-world tests have confirmed the usefulness of the suggested solutions in terms of efficiency, robustness, and practical applicability after they were implemented on an experimental platform based on the Robot Operating System (ROS).