A Localization System for Autonomous Driving: Global and Local Location Matching Based on Mono-SLAM

The utilization of the SLAM (Simultaneous Localization and Mapping) technique was extended from the robotics to the autonomous vehicles for achieving the positioning. However, SLAM cannot obtain the global position of the vehicle but a relative one to the start. For sake of this, a fast and accurate system was proposed to obtain both the local position and the global position of vehicles based on mono-SLAM which realized the SLAM by using monocular camera with a lower cost and power consumption. Firstly, the rough latitude and longitude of current position was obtained by using common GPS without differential signal. Then, the Mono-SLAM operated on the consecutive video frames to generate the localization and local trajectory and its accuracy was further improved by utilizing the IMU information. After that, a piece of Map centered in the rough position obtained by common GPS was downloaded from the Open Street Map. Finally, a searching process in the downloaded Map was executed by using chamfer matching algorithm to find a piece of path matched with the constructed trajectory. Consequently, the global position of the vehicle was obtained and the accumulated error can be decreased with cyclical searching. In the test, the performance of this proposed system outperforms current approaches in global location and its error was less than 5 meters, indicating in parallel the potential that mono-SLAM can bring to the global localization task.