LAUSR

Vision-based map-matching with HD map for high precision vehicle localization has gained great attention for its low-cost and ease of deployment. However, its localization performance is still unsatisfactory in accuracy and robustness in numerous real applications due to the sparsity and noise of the perceived HD map landmarks. This article proposes the tightly-coupled monocular map-matching localization algorithm (TM3Loc) for monocular-based vehicle localization. TM3Loc introduces semantic chamfer matching (SCM) to model monocular map-matching problem and combines visual features with SCM in a tightly-coupled manner. By applying the sliding window-based optimization technique, the historical visual features and HD map constraints are also introduced, such that the vehicle poses are estimated with an abundance of visual features and multi-frame HD map landmark features, rather than with single-frame HD map observations in previous works. Experiments are conducted on large scale dataset of 15 km long in total. The results show that TM3Loc is able to achieve high precision localization performance using a low-cost monocular camera, largely exceeding the performance of the previous state-of-the-art methods, thereby promoting the development of autonomous driving.