Mobile Robot Localization: a Modular, Odometry-Improving Approach
<|reference_start|>Mobile Robot Localization: a Modular, Odometry-Improving Approach: Despite the number of works published in recent years, vehicle localization remains an open, challenging problem. While map-based localization and SLAM algorithms are getting better and better, they remain a single point of failure in typical localization pipelines. This paper proposes a modular localization architecture that fuses sensor measurements with the outputs of off-the-shelf localization algorithms. The fusion filter estimates model uncertainties to improve odometry in case absolute pose measurements are lost entirely. The architecture is validated experimentally on a real robot navigating autonomously proving a reduction of the position error of more than 90% with respect to the odometrical estimate without uncertainty estimation in a two-minute navigation period without position measurements.<|reference_end|>
@article{mozzarelli2024mobile,
    title={Mobile Robot Localization: a Modular, Odometry-Improving Approach},
    author={Luca Mozzarelli, Luca Cattaneo, Matteo Corno, Sergio Matteo Savaresi},
    journal={arXiv preprint arXiv:2403.13452},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.13452},
    primaryClass={cs.RO}
}