Indoor Robot Mapping and Navigation System Based on Cyber-Physical Systems: Integration of SLAM Algorithm and Visual Information

Abstract

Synchronous Localization and Mapping (SLAM) and autonomous navigation technology are fundamental to the intelligence and automation of robots and they represent important research directions in intelligent transportation. This paper proposes an enhanced mapping algorithm based on Cyber-Physical Systems (CPS) to address the high precision demands of indoor robot autonomous navigation. The proposed system integrates a Kalman filter to fuse output data from multiple sensors, including a laser odometer, Inertial Measurement Unit (IMU), and wheel odometer, achieving precise multi-sensor fusion for localization. Additionally, the system combines the A<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\ast }$ </tex-math></inline-formula> algorithm with the pure pursuit autonomous navigation algorithm to optimize autonomous navigation performance. Visual information processing incorporates Yolov5 for data augmentation and scene recognition, thereby improving the system’s environmental perception. Experimental validation demonstrates an average mapping accuracy of 0.032 meters and an average navigation accuracy of 0.05 meters. The CPS architecture is highly effective for high-precision mapping and autonomous navigation in unknown and complex indoor environments, demonstrating the ability to improve localization and mapping accuracy in complex indoor environments.