OBJECT CLASSIFICATION USING CNN-BASED FUSION OF VISION AND LIDAR IN AUTONOMOUS VEHICLE ENVIRONMENT

Abstract

This paper presents an object classification method for vision and light detection and
ranging fusion of autonomous vehicles in the environment. This method is based on
convolutional neural network (CNN) and image upsampling theory. By creating a
point cloud of LIDAR data upsampling and converting into pixel-level depth
information, depth information is connected with Red Green Blue (RGB) data and fed
into a deep CNN. The proposed method can obtain informative feature representation
for object classification in autonomous vehicle environment using the integrated
vision and LIDAR data. This method is also adopted to guarantee both object
classification accuracy and minimal loss. Experimental results are presented and show
the effectiveness and efficiency of object classification strategies.