  In this paper, we propose a robust end-to-end multi-modal pipeline for place
recognition where the sensor systems can differ from the map building to the
query. Our approach operates directly on images and LiDAR scans without
requiring any local feature extraction modules. By projecting the sensor data
onto the unit sphere, we learn a multi-modal descriptor of partially
overlapping scenes using a spherical convolutional neural network. The employed
spherical projection model enables the support of arbitrary LiDAR and camera
systems readily without losing information. Loop closure candidates are found
using a nearest-neighbor lookup in the embedding space. We tackle the problem
of correctly identifying the closest place by correlating the candidates' power
spectra, obtaining a confidence value per prospect. Our estimate for the
correct place corresponds then to the candidate with the highest confidence. We
evaluate our proposal w.r.t. state-of-the-art approaches in place recognition
using real-world data acquired using different sensors. Our approach can
achieve a recall that is up to 10% and 5% higher than for a LiDAR- and
vision-based system, respectively, when the sensor setup differs between model
training and deployment. Additionally, our place selection can correctly
identify up to 95% matches from the candidate set.
