Visual Place Recognition Datasets for Indoor Spaces

Visual place recognition (VPR) is widely cast as a challenging image retrieval problem. Recently, many studies in this area have achieved superior results. However, most existing works consider outdoor spaces rather than indoor spaces. To fill this gap, this paper for the first time constructs a benchmark based on realistic environments for indoor VPR, involving three new indoor datasets which contains 25, 233 RGB images in total. These datasets cover typical indoor environments with over 250 places and provide a wide range of challenging cases. Moreover, this paper introduces a patch relation module based on spatial coordinate position of image patches and a global average pooling pyramid to get discriminative and robust features. Extensive experiments are conducted on our datasets to validate the effectiveness of the proposed method. The results indicate that indoor VPR in realistic setting is still challenging, fostering new research in this direction. Our dataset and code will be released at https://github.com/Dauntless-Wind/indoor-visual-place-recognition.