Global Localization using OpenStreetMap and Elevation Offsets
DOI:
https://doi.org/10.5753/jbcs.2024.3795Keywords:
Autonomous driving, data association, global localization, self-driving vehiclesAbstract
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.
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Copyright (c) 2024 André Przewodowski, Fernando Santos Osório, Valdir Grassi Júnior
This work is licensed under a Creative Commons Attribution 4.0 International License.
