3D Object Classification using Point Clouds and Deep Neural Network for Automotive Applications

Abstract

Object identification is a central part of autonomous cars and there are many sensors to help with this. One such sensor is the LIDAR which creates point clouds of the cars surrounding. This thesis evaluates a solution for object identification in 3D point clouds with the help of a neural network. A system named DELIS (DEtection in Lidar Systems), which takes a point cloud generated from a LIDAR as input, is designed. The system consists of two subsystems, one non-machine learning algorithm which segments the point cloud into clusters, one for each object, and a neural network that classifies this clusters. The final output is then the classes and the coordinates of the objects in the point cloud. The result of this thesis is a system named DELIS that can identify between pedestrians, cars, and cyclists.