Monocular Rotational Odometry with Incremental Rotation Averaging and Loop Closure

• URL: http://arxiv.org/abs/2010.01872v1
• Date: Mon, 5 Oct 2020 09:19:06 GMT
• Title: Monocular Rotational Odometry with Incremental Rotation Averaging and Loop Closure
• Authors: Chee-Kheng Chng, Alvaro Parra, Tat-Jun Chin, Yasir Latif
• Abstract summary: Estimating absolute camera orientations is essential for attitude estimation tasks. We 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.
• Score: 35.467052373502575
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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