Monocular Rotational Odometry with Incremental Rotation Averaging and Loop Closure

Estimating absolute camera orientations is essential for attitude estimation tasks. An established approach is to first carry out visual odometry (VO) or visual SLAM (V-SLAM), and retrieve the camera orientations (3 DOF) from the camera poses (6 DOF) estimated by VO or V-SLAM. One drawback of this approach, besides the redundancy in estimating full 6 DOF camera poses, is the dependency on estimating a map (3D scene points) jointly with the 6 DOF poses due to the basic constraint on structure-and-motion. To simplify the task of absolute orientation estimation, we formulate the monocular rotational odometry problem and devise a fast algorithm to accurately estimate camera orientations with 2D-2D feature matches alone. Underpinning our system is a new incremental rotation averaging method for fast and constant time iterative updating. Furthermore, our system maintains a view-graph that 1) allows solving loop closure to remove camera orientation drift, and 2) can be used to warm start a V-SLAM system. We conduct extensive quantitative experiments on real-world datasets to demonstrate the accuracy of our incremental camera orientation solver. Finally, we showcase the benefit of our algorithm to V-SLAM: 1) solving the known rotation problem to estimate the trajectory of the camera and the surrounding map, and 2) enabling V-SLAM systems to track pure rotational motions.