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Estimating absolute camera orientations is essential for attitude estimationtasks. An established approach is to first carry out visual odometry (VO) orvisual SLAM (V-SLAM), and retrieve the camera orientations (3 DOF) from thecamera 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 dependencyon estimating a map (3D scene points) jointly with the 6 DOF poses due to thebasic constraint on structure-and-motion. To simplify the task of absoluteorientation estimation, we formulate the monocular rotational odometry problemand devise a fast algorithm to accurately estimate camera orientations with2D-2D feature matches alone. Underpinning our system is a new incrementalrotation averaging method for fast and constant time iterative updating.Furthermore, our system maintains a view-graph that 1) allows solving loopclosure to remove camera orientation drift, and 2) can be used to warm start aV-SLAM system. We conduct extensive quantitative experiments on real-worlddatasets to demonstrate the accuracy of our incremental camera orientationsolver. Finally, we showcase the benefit of our algorithm to V-SLAM: 1) solvingthe known rotation problem to estimate the trajectory of the camera and thesurrounding map, and 2)enabling V-SLAM systems to track pure rotationalmotions.

Monocular Rotational Odometry with Incremental Rotation Averaging and Loop Closure