LiDAR-INS/GNSS-Based Real-Time Ground Removal, Segmentation, and Georeferencing Framework for Smart Transportation

Abstract

Light Detection and Ranging (LiDAR) sensor is attracting significant attention in the field of Smart Transportation because of its ability to give depth information. It is already extensively used for obstacle detection. Inertial Navigation System (INS) with Global Navigation Satellite System (GNSS) gives the global position, orientation, and velocity of a given object in real-time. In autonomous driving, LiDAR point clouds are segmented to find out the positions of various objects in the surrounding of the vehicle of interest. However, the obstacle position is determined in local ENU (East North Up) coordinates. In this paper, an end-to-end framework has been presented, which takes input from LiDAR and INS/GNSS system and gives the obstacle position in universal coordinates (latitude, longitude) in real-time. The proposed framework includes ground point removal from raw LiDAR data, obstacle segmentation, and LiDAR data fusion with INS/GNSS data for georeferencing. For LiDAR data processing, a novel method for removing ground points has been presented, in which the entire point cloud is divided into square grids in the horizontal plane. Based on the statistics of the vertical distribution of the points in each grid, ground points are identified. Two ground point datasets were also created for testing the proposed algorithm. These datasets contain point clouds of ground with varying inclination from various multi-channel LiDARs. The proposed ground removal method was tested on Paris-Lille-3D dataset and the dataset created. An F1-score greater than 0.99 and 0.98 was achieved on our dataset and Paris Lille-3D dataset respectively. The framework is tested on different hardware configurations and found to be suitable for real-time applications.