A Study on the SLAM of Automotive Vehicles Using Bumper-Mounted Dual LiDAR

Abstract

This study proposes a localization and three-dimensional (3D) mapping algorithm for autonomous vehicles using bumper-mounted light detection and ranging (LiDAR) sensors. Traditional roof-mounted LiDAR systems provide a wide field of view (FOV) but have drawbacks such as increased vehicle height, reduced aerodynamics, and greater exposure to environmental factors. The proposed approach addresses these challenges by installing two LiDAR sensors at the ends of the vehicle bumper, optimizing the bumper-level FOV while minimizing interference from ground-level obstacles. The algorithm integrates point cloud data from both sensors using iterative closest point (ICP) matching and feature extraction techniques, enabling accurate vehicle odometry and 3D mapping. Real-world experiments were conducted using a test vehicle equipped with the proposed system. The results demonstrate that the bumper-mounted configuration provides localization accuracy comparable to roof-mounted systems, especially in the XY plane, despite minor Z-axis discrepancies. Additionally, the proposed system expands the horizontal FOV through dual LiDAR placement, addressing the limitation of rear-view coverage. These findings suggest that the bumper-mounted LiDAR system offers a practical and reliable solution for autonomous driving, enhancing robustness against environmental challenges while maintaining mapping accuracy. This study confirms the potential of bumper-mounted LiDAR configurations for real-world applications in autonomous vehicle technology.