Light-weight visual place recognition using convolutional neural network for mobile robots

Abstract

Place recognition is one of the essential components for mobile robot navigation. In this work, we present a light-weight convolutional neural network (CNN) approach for visual place recognition. Our proposed approach specifically targets embedded systems. To reduce the computational complexity in the network, we design a fully convolutional network architecture with fewer layers and filters. The proposed network directly learns a vector space where its distance corresponds to place similarity by metric learning. For effective training, we adopt triplet embedding with image dataset captured at various viewpoints. Such approach allows the network to embed scenes directly without any further training process on robots. The experimental results show that the proposed method significantly outperforms conventional algorithms including other CNN-based approaches in terms of accuracy and computational time.