Projection Mapping Segmentation Block: A Fusion Approach of Pointcloud and Image for Multi-Objects Classification

Abstract

There is an increasing trend towards multi-modal sensor fusion to improve the perception capability of autonomous vehicles, such as the fusion of light detection and ranging (LiDAR) pointcloud and camera image data. In this work, we proposed the early-fusion method of projection mapping segmentation blocks, which is a method to map spatial pointcloud segmentation blocks and image pixel segmentation blocks to each other according to the projection relationship. The local spatial association feature and the local semi-variance texture feature were additionally designed for feature extraction, and each pair of segmentation blocks is used as a processing unit for feature extraction. Finally, the classification task was completed by Support Vector Machines (SVM) algorithm. We compared the classification results of the single pointcloud without fusion and pointcloud+image fusion under two segmentation methods: box-based and sector ring hexahedron-based. Our findings suggest that the data fusion classification method based on sector ring hexahedron segmentation not only improves classification accuracy but also reduces computational resources, yielding encouraging results.

Keywords

• Environment perception
• multi-objects classification
• data fusion
• SVM