  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 \textit{TF-VPR} that uses
temporal neighborhoods and learnable feature neighborhoods to discover unknown
spatial neighborhoods. Our method follows an iterative training paradigm which
alternates between: (1) representation learning with data augmentation, (2)
positive set expansion to include the current feature space neighbors, and (3)
positive set contraction via geometric verification. We conduct comprehensive
experiments on both simulated and real datasets, with either RGB images or
point clouds as inputs. The results show that our method outperforms our
baselines in recall rate, robustness, and heading diversity, a novel metric we
propose for VPR. Our code and datasets can be found at
https://ai4ce.github.io/TF-VPR/.
