Design and Implementation of Efficient SLAM System Based on Multi-Sensor Fusion

In Simultaneous Localization and Mapping (SLAM) algorithms, the robustness of single sensors is relatively poor. To address this issue, this study develops a multi-sensor fusion platform that integrates data from LiDAR, stereo cameras, and IMUs, and employs advanced algorithms such as ORB-SLAM2 and Cartographer for efficient data processing and map construction. For point cloud data, we apply the Iterative Closest Point (ICP) algorithm for point-to-point optimization, effectively solving the issue of large map accumulation errors in traditional LiDAR-based SLAM algorithms, which rely solely on odometry for estimating the robot's pose. By using publicly available datasets and conducting multiple map construction experiments in a real campus environment, the proposed method's effectiveness and feasibility are validated, significantly reducing error accumulation and producing more accurate localization and map construction results.