Low-complexity adaptive radius outlier removal filter based on PCA forlidar point cloud denoising

In autonomous driving, cars rely on light detection and ranging (lidar) to navigate the surroundings, but interference from the environment makes it difficult to retrieve useful information. To address this problem, this paper develops a noise reduction method to filter lidar point clouds (i.e., an adaptive radius outlier removal filter based on principal component analysis). We believe this method can outperform existing clustering algorithms when applied to point cloud images captured at a large distance from the lidar. Compared to traditional methods, the proposed method has higher precision and recall with an F-score up to 0.876 and complexity reduced by at least 50%.