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.

通过 2D-2D 特征匹配技术，利用旋转平均算法设计一种可快速而又准确地估算单目相机旋转方向的系统，该系统通过维护一个视图图形并解决旋转问题来估算摄像机轨迹和地图，并驱动 V-SLAM 系统跟踪纯旋转运动。