Comparison of camera-based and 3D LiDAR-based place recognition across weather conditions

Abstract

Place recognition 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 place recognition. The processing for all data inputs is performed using a similar architecture of a neural network and is evaluated 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 place recognition to increase its performance. This proves that there is a need for more research into place recognition performed with multi-sensory setups.