Quality Evaluation for 3D Point Cloud Maps Using Entropy Focused on Road Surfaces

Current techniques for assessing the accuracy of 3D point cloud maps are often computationally demanding and lack the capability to pinpoint regions with diminished accuracy. In this research, we introduce an effective approach for evaluating the accuracy of 3D point cloud maps intended for autonomous driving systems. Our method focuses on calculating the entropy of the road surface along the vehicle's trajectory. By employing a moving average of Mean Map Entropy (MME), we can automatically identify areas where the map accuracy has degraded while also reducing the computational load. Through our evaluation, we demonstrate that the proposed method effectively detects point cloud blurring and surface duplication caused by SLAM/MMS errors.