Non-Lambertian Surfaces and Their Challenges for Visual SLAM

Abstract

Non-Lambertian surfaces are special surfaces that can cause specific type of reflectances called specularities, which pose a potential issue in industrial SLAM. This article reviews fundamental surface reflectance models, modern state-of-the-art computer vision algorithms and two public datasets, KITTI and DiLiGenT, related to non-Lambertian surfaces' research. A new dataset, SPINS, is presented for the purpose of studying non-Lambertian surfaces in navigation and an empirical performance evaluation with ResNeXt-101-WSL, ORB SLAM 3 and TartanVO is performed on the data. The article concludes with discussion about the results of empirical evaluation and the findings of the survey.

Keywords

• Monocular depth estimation
• navigation
• computer vision
• non-Lambertian surfaces
• specularities