We propose a novel real-time direct monocular visual odometry for omnidirectional cameras. Our method extends direct sparse odometry by using the unified omnidirectional model as a projection function, which can be applied to fisheye cameras with a field-of-view (FoV) well above 180 • . This formulation allows for using the full area of the input image even with strong distortion, while most existing visual odometry methods can only use a rectified and cropped part of it. Model parameters within an active keyframe window are jointly optimized, including the intrinsic/extrinsic camera parameters, three-dimensional position of points, and affine brightness parameters. Thanks to the wide FoV, image overlap between frames becomes bigger and points are more spatially distributed. Our results demonstrate that our method provides increased accuracy and robustness over state-ofthe-art visual odometry algorithms. Index Terms-SLAM, omnidirectional vision, visual-based navigation.
Similarly, H. Matsuki et al. REF extended DSO for omnidirectional cameras by using the unified camera model as a projection function.
Omnidirectional DSO: Direct Sparse Odometry With Fisheye Cameras
IEEE Robotics and Automation Letters
IEEE Robotics and Automation Letters
Computer Science Engineering