Place Recognition with Memorable and Stable Cues for Loop Closure of Visual SLAM Systems

Visual Place Recognition (VPR) is a fundamental yet challenging task in Visual Simultaneous Localization and Mapping (V-SLAM) problems. The VPR works as a subsystem of the V-SLAM. VPR is the task of retrieving images upon revisiting the same place in different conditions. The problem is even more difficult for agricultural and all-terrain autonomous mobile robots that work in different scenarios and weather conditions. Over the last few years, many state-of-the-art methods have been proposed to solve the limitations of existing VPR techniques. VPR using bag-of-words obtained from local features works well for a large-scale image retrieval problem. However, the aggregation of local features arbitrarily produces a large bag-of-words vector database, limits the capability of efficient feature learning, and aggregation and querying of candidate images. Moreover, aggregating arbitrary features is inefficient as not all local features equally contribute to long-term place recognition tasks. Therefore, a novel VPR architecture is proposed suitable for efficient place recognition with semantically meaningful local features and their 3D geometrical verifications. The proposed end-to-end architecture is fueled by a deep neural network, a bag-of-words database, and 3D geometrical verification for place recognition. This method is aware of meaningful and informative features of images for better scene understanding. Later, 3D geometrical information from the corresponding meaningful features is computed and utilised for verifying correct place recognition. The proposed method is tested on four well-known public datasets, and Micro Aerial Vehicle (MAV) recorded dataset for experimental validation from Victoria Park, Adelaide, Australia. The extensive experimental results considering standard evaluation metrics for VPR show that the proposed method produces superior performance than the available state-of-the-art methods.