Enhancing Autonomous Robot Perception Via Slam Coupled With AI-Driven Selective Obstacle Object Detection

Abstract

Autonomous mobile robots are changing various industries, making them more efficient and adaptable. They are used in critical sectors such as manufacturing, healthcare, logistics, and infrastructure to make operations smoother, increase productivity, and make the economy more resilient. This is done through Robotics Automation Systems (RAS). The research presented in this thesis addresses a pivotal problem in the field of mobile robotics, specifically the limitations of conventional Simultaneous Localization and Mapping (SLAM) algorithms in accurately recognizing and mapping various types of obstacles and hazards. Existing methodologies often lack the computational intelligence to differentiate between physical and non-physical obstacles, leading to suboptimal navigational decisions and posing safety risks. To rectify these shortcomings, the proposed solution unfolds in a structured five-stage approach: AI-based object detection, stereo vision, disparity and depth estimation, dimension estimation, and SLAM integration. The AI models achieved an accuracy rate of 0.975 for bottle obstacle detection and 0.906 for fire flame identification. Depth and dimension estimation stages employed stereo vision techniques to attain an accuracy of 94% and 96.7%, respectively, for objects within a 6-meter radius of the robot. Including a GPS-less capability reduced locational error to 0.3 meters, outperforming traditional GPS modules with up to 5-meter errors. The concluding stage integrates these advancements into the SLAM-generated occupancy grid, enhancing the robot’s environmental mapping and navigational capabilities. The research successfully proposed an autonomous mobile robot system that can visually distinguish obstacles, pinpoint their location and dimensions, and deftly navigate around them, enhancing its operational autonomy and safety in complex environments.