360VO: Visual Odometry Using A Single 360 Camera
Huajian Huang, Sai-Kit Yeung
Abstract
In this paper, we propose a novel direct visual odometry algorithm to take the advantage of a 360-degree camera for robust localization and mapping. Our system extends direct sparse odometry by using a spherical camera model to process equirectangular images without rectification to attain omnidirectional perception. After adapting mapping and optimization algorithms to the new model, camera parameters, including intrinsic and extrinsic parameters, and 3D mapping can be jointly optimized within the local sliding window. In addition, we evaluate the proposed algorithm using both real world and large-scale simulated scenes for qualitative and quantitative validations. The extensive experiments indicate that our system achieves start of the art results.