  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.
