Global Localization using OpenStreetMap and Elevation Offsets

Fernando Santos Osório

Abstract:

Localization is a critical component in autonomous vehicle navigation stacks. While GNSS-only localization
cannot be fully reliable and available all the time, localization based on 3D high-definition (HD) maps have
to be robust to world changes, which is still a challenging issue. Added to that, in general, HD maps are expensive
and difficult to construct and maintain. In this paper, we propose a particle filter-based 2D global pose estimation
method that can use the crowdsourced OpenStreetMap (OSM) API, a digital surface map, or both. The main contributions
of the proposed approach are: that it is lightweight, does not require the vehicle to map the environment,
does not require a GPU (can be used with low-power computing resources), is agnostic to the odometry source,
and achieved relatively low position and orientation errors for this localization modality using the KITTI dataset
sequences. The proposed method’s implementation is open source and is available with the experimental results on
our GitHub page.