Fast Initialization for Monocular Map Matching Localization via Multi-lane Hypotheses in Highway Scenarios

Many researchers have used the map-matching algorithm to leverage inadequate traditional vehicle localization with HD maps for fast and efficient vehicle localization. The initialization process in the map-matching algorithm has always been problematic due to the nature of GNSS error, which might exceed the average lane width of approximately 3.5m. Thus, directly using the GNSS data to obtain the rough initial pose may cause the failure of the initialization process. The general solution is to randomly sample around the GNSS data and test the initialization with these hypotheses. However, this often leads to expensive computation and thus fails to run in realtime or online mode, as a dense sampling is required to achieve an acceptable level of initial estimation accuracy. As a viable alternative, we propose a multi-lane hypotheses approach to narrow down the search by limiting the sample pose to the number of lanes within the GNSS data's error radius. From these poses, we then perform an efficient map-matching to refine these pose candidates. Moreover, a novel belief function to evaluate the hypothesis is proposed to select the best hypothesis for system initialization robustly. Our evaluation result shows that we have outperformed the primary random sampling method in both accuracy and efficiency.