Vision-based motion estimation and 3D reconstruction, which have numerous
applications (e.g., autonomous driving, navigation systems for airborne devices
and augmented reality) are receiving significant research attention. To
increase the accuracy and robustness, several researchers have recently
demonstrated the benefit of using large field-of-view cameras for such
applications. In this paper, we provide an extensive review of existing models
for large field-of-view cameras. For each model we provide projection and
unprojection functions and the subspace of points that result in valid
projection. Then, we propose the Double Sphere camera model that well fits with
large field-of-view lenses, is computationally inexpensive and has a
closed-form inverse. We evaluate the model using a calibration dataset with
several different lenses and compare the models using the metrics that are
relevant for Visual Odometry, i.e., reprojection error, as well as computation
time for projection and unprojection functions and their Jacobians. We also
provide qualitative results and discuss the performance of all models