Improving Visual Place Recognition Based Robot Navigation Through Verification of Localization Estimates
<|reference_start|>Improving Visual Place Recognition Based Robot Navigation Through Verification of Localization Estimates: Visual Place Recognition (VPR) systems often have imperfect performance, which affects robot navigation decisions. This research introduces a novel Multi-Layer Perceptron (MLP) integrity monitor for VPR which demonstrates improved performance and generalizability over the previous state-of-the-art SVM approach, removing per-environment training and reducing manual tuning requirements. We test our proposed system in extensive real-world experiments, where we also present two real-time integrity-based VPR verification methods: an instantaneous rejection method for a robot navigating to a goal zone (Experiment 1); and a historical method that takes a best, verified, match from its recent trajectory and uses an odometer to extrapolate forwards to a current position estimate (Experiment 2). Noteworthy results for Experiment 1 include a decrease in aggregate mean along-track goal error from ~9.8m to ~3.1m in missions the robot pursued to completion, and an increase in the aggregate rate of successful mission completion from ~41% to ~55%. Experiment 2 showed a decrease in aggregate mean along-track localization error from ~2.0m to ~0.5m, and an increase in the aggregate precision of localization attempts from ~97% to ~99%. Overall, our results demonstrate the practical usefulness of a VPR integrity monitor in real-world robotics to improve VPR localization and consequent navigation performance.<|reference_end|>
@article{claxton2024improving,
    title={Improving Visual Place Recognition Based Robot Navigation Through
  Verification of Localization Estimates},
    author={Owen Claxton, Connor Malone, Helen Carson, Jason Ford, Gabe Bolton,
  Iman Shames, Michael Milford},
    journal={IEEE Robotics and Automation Letters 2024},
    year={2024},
    doi={10.1109/LRA.2024.3483045},
    archivePrefix={arXiv},
    eprint={2407.08162},
    primaryClass={cs.CV cs.RO}
}