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.

翻译：视觉地点识别（VPR）系统通常存在性能不完善的问题，这会影响机器人的导航决策。本研究提出了一种新颖的用于VPR的多层感知机（MLP）完整性监测器，相较于先前最先进的SVM方法，该监测器展现出更优的性能与泛化能力，同时消除了针对特定环境的训练需求并减少了人工调参工作量。我们在大量真实世界实验中测试了所提出的系统，并同时提出了两种基于完整性的实时VPR验证方法：一种用于机器人导航至目标区域的即时拒绝方法（实验1）；以及一种历史方法，该方法从其近期轨迹中选取一个最佳且已验证的匹配点，并利用里程计向前推算出当前位置估计（实验2）。实验1的显著成果包括：在机器人执行并完成的任务中，沿航迹目标误差的总体平均值从约9.8米降低至约3.1米，任务成功完成率的总体平均值从约41%提升至约55%。实验2显示，沿航迹定位误差的总体平均值从约2.0米降低至约0.5米，定位尝试的总体精确率从约97%提升至约99%。总体而言，我们的结果证明了VPR完整性监测器在真实世界机器人技术中，对于提升VPR定位及后续导航性能具有实际应用价值。