Experimental study of odometry estimation methods using RGB-D cameras

Lightweight RGB-D cameras that can provide rich 2D visual and 3D point cloud information are well suited to the motion estimation of indoor micro aerial vehicles (MAVs). In recent years, several RGB-D visual odometry methods which process data from the sensor in different ways have been proposed. However, it is unclear which methods are preferable for online odometry estimation on a computation-limited, fast moving MAV in practical indoor environments. This paper presents a detailed analysis and comparison of several state-of-the-art real-time odometry estimation methods in a variety of challenging scenarios, with a special emphasis on the trade-off among accuracy, robustness and computation speed. An experimental comparison is conducted using public available benchmark datasets and author-collected datasets including long corridors, illumination changing environments and fast motion scenarios. Experimental results present both quantitative and qualitative differences among these methods and provide some guidelines on choosing the “right” algorithm for an indoor MAV according to the quality of the RGB-D data and environment characteristics.