Depth representation of lidar point cloud with adaptive surface patching for object classification

Abstract

Object segmentation and classification from point cloud light detection and ranging (LiDAR) are increasingly important in 3D mapping and autonomous mobile systems. Even though the distance measurement and object localization from laser pulses are accurate and robust to environmental variations better than an image, the reflected points in each frame are sparse and lack semantic information. The appropriate representation that can extract object characteristics from a single frame point cloud is important for segmenting a moving object before it makes a trail in the reconstruction. We propose depth projection and an adaptive surface patch to extract and emphasize shape, curve, and some texture of the object point cloud for classification. The projection plane is based on the sensor position to ensure that the projected image contains fine details of the object surface. An adaptive surface patch is used to construct an object surface from a sparse point cloud at any distance. The experimental results indicate that the object representation can be used to classify an object by means of an existing image classification method [1].