Visualization of 3D Point Clouds for Vehicle Detection Based on LiDAR and Camera Fusion

Abstract

Obstacle detection problem is the most studied problem in computer vision. Methodologies of object detection generally work in a single modality, such as vision, Light Detection and Ranging (LiDAR), or laser. Multiple modalities like LiDAR with Camera are mainly used in robotics and automation. LiDAR is the best way for creating point clouds which are crucial for Object Detection. Object Detection systems currently deploy various deep learning models. In this paper, the detection of vehicles is proposed using Camera and Lidar data. Preprocessing is done with the help of the open3d library. Random sample consensus (RANSAC), Density-based spatial clustering of applications with noise (DBSCAN) Algorithms are used for visualization and clustering of the LiDAR point cloud. The detection of potential vehicles as clusters is proposed in this paper. Major datasets for this purpose are KITTI, Waymo Open Dataset, and the Lyft Level 5 AV Dataset.