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Place recognition plays a crucial role in navigational assistance, and isalso a challenging issue of assistive technology. The place recognition isprone to erroneous localization owing to various changes between database andquery images. Aiming at the wearable assistive device for visually impairedpeople, we propose an open-sourced place recognition algorithm OpenMPR, whichutilizes the multimodal data to address the challenging issues of placerecognition. Compared with conventional place recognition, the proposed OpenMPRnot only leverages multiple effective descriptors, but also assigns differentweights to those descriptors in image matching. Incorporating GNSS data intothe algorithm, the cone-based sequence searching is used for robust placerecognition. The experiments illustrate that the proposed algorithm manages tosolve the place recognition issue in the real-world scenarios and surpass thestate-of-the-art algorithms in terms of assistive navigation performance. Onthe real-world testing dataset, the online OpenMPR achieves 88.7% precision at100% recall without illumination changes, and achieves 57.8% precision at 99.3%recall with illumination changes. The OpenMPR is available athttps://github.com/chengricky/OpenMultiPR.

OpenMPR: Recognize Places Using Multimodal Data for People with Visual Impairments