Fast monocular vision place recognition for non‐uniform vehicle speed and varying lighting environment

Abstract

This paper presents a novel Fast Monocular Visual Place Recognition (FMPR) with a shallow path-oriented offline learning stage and an online place recognition and tracking stage. FMPR uses a tube of frames with a humanlike key frame recognition to solve place recognition for situations with varying speeds and changing lighting conditions, which are two most commonly encountered situations in real life. We propose an offline learning to analyze the correlation of all video frames in a reference path and to extract effective feature patches of key frames with an offline feature-shifts approach to achieve real-time place recognition. Our recognition results are on the basis of both the instant feature matching of frames and the historical recognition results which impose temporal logic constraints on the movement of a vehicle. Experimental results demonstrate that our proposed method can achieve comparable or even better performance compared with the state-of-the-art methods on different challenging datasets, especially for the case which requires a trade-off between the performance and the processing time. We believe that our FMPR offers a useful alternative to computationally expensive deep learning-based methods especially for applications with battery-powered or resource-limited devices.