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