BEV Image-based Lane Tracking Control System for Autonomous Lane Repainting Robot

In this paper, we present a novel study on a BEV (bird’s eye view) image-based lane tracking control system for an autonomous lane repainting robot. Our research introduces a cutting-edge lane detection method based on BEV images, leveraging row-anchor techniques to enhance precision and provide detailed error information for lane tracking algorithms. By utilizing real-time sensor data and advanced deep learning processes, we have successfully implemented a high-performance lane repainting system that minimizes errors and ensures accuracy. Our proposed position-based visual pure pursuit algorithm (PV-PP) plays a crucial role in guiding the lane repainting process with precision and efficiency, ultimately improving the functionality and feasibility of the linear actuator responsible for paint spraying in the real indusrial fields. Through our contributions, including innovative lane detection methods, real-time sensor utilization, and robot control algorithm design, we aim to advance the field of autonomous lane repainting robots and enhance the safety and effectiveness of road maintenance operations.