Experimental Evaluation of RGB-D Visual Odometry Methods

Abstract

RGB-D cameras that can provide rich 2D visual and 3D depth information are well suited to the motion estimation of indoor mobile robots. In recent years, several RGB-D visual odometry methods that process data from the sensor in different ways have been proposed. This paper first presents a brief review of recently proposed RGB-D visual odometry methods, and then presents a detailed analysis and comparison of eight state-of-the-art real-time 6DOF motion estimation methods in a variety of challenging scenarios, with a special emphasis on the trade-off between accuracy, robustness and computation speed. An experimental comparison is conducted using publicly available benchmark datasets and author-collected datasets in various scenarios, including long corridors, illumination changing environments and fast motion scenarios. Experimental results present both quantitative and qualitative differences between these methods and provide some guidelines on how to choose the right algorithm for an indoor mobile robot according to the quality of the RGB-D data and environmental characteristics.