Local and Deep Features for Robust Visual Indoor Place Recognition

Abstract

This study focuses on the area of visual indoor place recognition (e.g., in an office setting, automatically recognizing different places, such as offices, corridor, wash room, etc.). The potential applications include robot navigation, augmented reality, and image retrieval. However, the task is extremely demanding because of the variations in appearance in such dynamic setups (e.g., view-point, occlusion, illumination, scale, etc.). Recently, Convolutional Neural Network (CNN) has emerged as a powerful learning mechanism, able to learn deep higher-level features when provided with a comparatively big quantity of labeled training data. Here, we exploit the generic nature of CNN features for robust visual place recognition in the challenging COLD dataset. So, we employ the pre-trained CNNs (on the related tasks of object and scene classification) for deep feature extraction in the COLD images. We demonstrate that these off-the-shelf features, when combined with a simple linear SVM classifier, outperform their bag-of-features counterpart. Moreover, a simple combination scheme, combining the local bag-of-features and higher-level deep CNN features, produce outstanding results on the COLD dataset.