An Improved Registration Method for Radar Point Cloud in Weakly Structured Texture Scenes of Urban Environments

Radar point cloud registration is vital for various applications in autonomous driving, robotics, and localization. However, existing methods have not adequately accounted for weakly structured texture scenes in urban environments, such as long-distance monotonous walls and open roads, which are prevalent in urban environments. Failures in point cloud registration in these scenarios can significantly affect the performance of the aforementioned applications. In order to improve the registration accuracy in weakly structured texture scenes of urban environments, we propose a novel mismatch elimination and an improved sample consensus registration method. The experimental results demonstrate that the proposed method achieves higher registration accuracy in weakly structured texture scenes than the compared methods.