Self-Supervised Place Recognition by Refining Temporal and Featural Pseudo Labels from Panoramic Data

Understand

Visual place recognition (VPR) using deep networks has achieved state-of-the-art performance.

• However, most of them require a training set with ground truth sensor poses to obtain positive and negative samples of each observation's spatial neighborhood for supervised learning.
• When such information is unavailable, temporal neighborhoods from a sequentially collected data stream could be exploited for self-supervised training, although we find its performance suboptimal.
• Inspired by noisy label learning, we propose a novel self-supervised framework named TF-VPR that uses temporal neighborhoods and learnable feature neighborhoods to discover unknown spatial neighborhoods.