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Loop closure based on camera images provides excellent results onbenchmarking datasets, but might struggle in real-world adverse weatherconditions like direct sun, rain, fog, or just darkness at night. In automotiveapplications, the sensory setups include 3D LiDARs that provide informationcomplementary to cameras. The presented article focuses on the evaluation ofcamera-based, LiDAR-based, and joint camera-LiDAR-based loop closures applyinga similar processing pipeline consisting of a neural network under varyingweather conditions using the newly available USyd dataset. The experimentsperformed on the same trajectories in diverse weather conditions over 50 weeksprove that a 16-line 3D LiDAR can be used to supplement image-based loopclosure to increase loop closure performance. This proves that there is a needfor more research into loop closures performed with multi-sensory setups.

Comparison of camera-based and 3D LiDAR-based loop closures across weather conditions