Detection of changes in maps using LiDAR point clouds

Abstract

Self-driving cars are one of the most vibrant fields of Robotics and applied Artificial Intelligence. At the current level of the industry development, maps are the main source of the vehicle’s knowledge of the surrounding environment. Providing basic knowledge for the motion planning and global navigation, they are an essential part of the self-driving car’s autonomy. Therefore, they should be kept up to date. Locations that have changed need to be visited again and remapped. Change detection can be done by comparing two point clouds obtained at the same spatial location, but at different points of time. For the pairwise comparison of the point clouds, first we should select the overlapping road parts, then the alignment is performed and finally the distance metrics are calculated. The aim of my thesis is to develop a pipeline that detects changes along the route of the autonomous car. The pipeline includes dynamic object filtering, point clouds alignment and evaluation of their difference. The developed pipeline is evaluated on the Oxford RobotCar dataset since it contains different routes through the same places for many month, which can provide a set of significant changes on the road. To our knowledge, there were no previous attempts to create an automated pipeline for the map’s change detection with the LiDAR point clouds. The results show that the constructed pipeline can detect significant changes with the LiDAR input data. The developed pipeline is the first step towards eventually ensuring real-time updates of the map for self-driving vehicles, which will help cars to operate in the city more optimally.