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arXiv · 2402.00330

Night-Rider: Nocturnal Vision-aided Localization in Streetlight Maps Using Invariant Extended Kalman Filtering

Tianxiao Gao·Mingle Zhao·Chengzhong Xu·Hui Kong
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Abstract

Vision-aided localization for low-cost mobile robots in diverse environments has attracted widespread attention recently. Although many current systems are applicable in daytime environments, nocturnal visual localization is still an open problem owing to the lack of stable visual information. An insight from most nocturnal scenes is that the static and bright streetlights are reliable visual information for localization. Hence we propose a nocturnal vision-aided localization system in streetlight maps with a novel data association and matching scheme using object detection methods. We leverage the Invariant Extended Kalman Filter (InEKF) to fuse IMU, odometer, and camera measurements for consistent state estimation at night. Furthermore, a tracking recovery module is also designed for tracking failures. Experimental results indicate that our proposed system achieves accurate and robust localization with less than $0.2\%$ relative error of trajectory length in four nocturnal environments.

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Tianxiao Gao, Mingle Zhao, Chengzhong Xu, Hui Kong. 2024-03-03. Night-Rider: Nocturnal Vision-aided Localization in Streetlight Maps Using Invariant Extended Kalman Filtering. https://arxiv.org/abs/2402.00330

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