A novel noise-robustness and rotation-invariant LADAR point cloud target classification method

Identifying targets of LAser Detection And Ranging (LADAR) point clouds that are affected by noise and different poses throws a significant challenge. This paper proposes a novel LADAR point cloud target classification method that is both noise-robust and rotation-invariant. Specifically, the proposed method transforms the point cloud target into a slice image that is resilient to noise and holds rotation-invariance. Next, a designed 2D Convolutional Neural Network (CNN) is utilized to classify the corresponding point cloud target based on the slice image. To overcome the challenge of lacking prior knowledge about the discriminative features of the slice image, the 2D CNN is designed with the Local Importance-based Pooling (LIP) layer. This layer extracts the discriminative feature in a data-driven manner, thereby improving the accuracy of the classification process. The proposed method is evaluated through multiple experiments on the public ModelNet40 dataset. The experimental results demonstrate that the designed LIP-CNN can better learn the discriminative features of the slice image, achieving high classification accuracy. Moreover, the proposed slice-image-based method is capable of accurately classifying the target, even in the presence of noise and different poses.