Multi-Sensor Fusion Approach for Calibrating 2D Lidar Distance Measurement via Fastsam Image Segmentation

This paper introduces a method that integrates LiDAR and FastSAM for precise distance measurement, object identification, and segmentation. In the fields of computer vision and intelligent robotics, accurate identification and localization of objects of interest are crucial. LiDAR provides the necessary distance information; however, the signals emitted by LiDAR diverge as the distance increases, potentially leading to inaccurate distance information. Therefore, this paper employs FastSAM image segmentation to assist LiDAR in distance measurement by segmenting target objects from images, thereby obtaining accurate distance information. Experimental results demonstrate that this method effectively fuses distance information with image data. By segmenting images to obtain object information and complementing LiDAR's depth information, the method accurately performs distance measurement and object identification. This technology has broad application prospects in areas such as robot navigation and autonomous driving, laying the foundation for intelligent systems to perceive the environment and understand scenes. Future work will focus on further improving the accuracy of distance measurement and exploring the potential of integrating additional sensors.