In this paper, we propose an accurate lane-level map building method using low-cost sensors such as cameras, GPS and in-vehicle sensors. First, we estimate the ego-motion from the stereo camera and the in-vehicle sensors, and globally optimize the accurate vehicle positions by fusion with the GPS data. Next, we perform lane detection on every image frame in the camera. Lastly, we repeatedly accumulate and cluster the detected lanes based on the accurate vehicle positions, and perform polyline fitting algorithm. The polyline fitting algorithm follows a variant of the Random Sample Consensus (RANSAC) algorithm, which particularly solves the multi-line fitting problem. This algorithm can expand the lane area and improve the accuracy at the same time by repeatedly driving the same road. We evaluated the lane-level map building on two types of roads: a proving ground and a real driving environment. The lane map accuracy verified at the proving ground was 9.9982cm of the CEP error.

Date Published: 15 March 2019
PDF: 5 pages
Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 1104129 (15 March 2019); doi: 10.1117/12.2522747

Published in SPIE Proceedings Vol. 11041:
Eleventh International Conference on Machine Vision (ICMV 2018)
Antanas Verikas; Dmitry P. Nikolaev; Petia Radeva; Jianhong Zhou, Editor(s)