Self-Supervised Lidar Place Recognition in Overhead Imagery Using Unpaired Data

—As much as place recognition is crucial for navi- gation, mapping and collecting training ground truth, namely sensor data pairs across different locations, are costly and time- consuming. This paper tackles these by learning lidar place recognition on public overhead imagery and in a self-supervised fashion, with no need for paired lidar and overhead imagery data. We learn the cross-modal data comparison between lidar and overhead imagery with a multi-step framework. First, images are transformed into synthetic lidar data and a latent projection is learned. Next, we discover pseudo pairs of lidar and satellite data from unpaired and asynchronous sequences, and use them for training a final embedding space projection in a cross-modality place recognition framework. We train and test our approach on real data from various environments and show performances approaching a supervised method using paired data.