## 2 Related Works
### Omnidirectional Visual Odometry  
To improve the motion estimation accuracy in the VO context, the extraction of reliable information about the world scene is crucial. This way, the use of omnidirectional cameras in this field has become more and more common. If a raw omnidirectional image stream is used, on one hand, a deeper knowledge about the environment is obtained, but on the other hand, it is required to deal with the distortion present in these images.  
Many works that use omnidirectional cameras in VO have been proposed in the literature. Some state-of-the-art original VO methods were extended to the use of these devices. For example, direct sparse odometry (DSO) [12] was adapted in this way. The omnidirectional version [13] uses a full image area even in the presence of strong distortion. It takes advantage of the high number of point overlap between images due to the higher field of view. In the same way, LSD-SLAM [14] was extended. The new version [15] uses raw omnidirectional images to extract more information about the world, which results in a performance improvement even in pure rotations. They use a 185\({}^{\circ}\) fisheye lens to test and evaluate their work. [PERSON] et al. propose a version of semi-direct visual odometry (SVO) [16] that also uses wide field of view cameras. They implement a camera model suitable for these cameras and use samples from the epipolar curves present in the omnidirectional VO configuration to estimate the camera motion [7].  
Some built from origin omnidirectional VO methods are also present in the literature. For example, [PERSON] et al. presented a catadioptric camera configuration for planetary rovers in GPS-denied environments [17]. In this work, two approaches are proposed: the first is optical-flow-based; the second is structure-for-motion-based. To evaluate their approach, a sequence with 2000 images is used. Similarly, two different works present the same configuration [18; 19]. The first is a monocular VO system that uses an omnidirectional camera placed on top of a car to estimate motion in outdoor environments. This system presents two different trackers--a feature-based that uses scale-invariant feature transform (SIFT) features and an appearance-based that works as a visual compass. In the second, they use four cameras with aligned optical centers to simulate an omnidirectional one. The rotation and translation components were decoupled to estimate the robot pose. This approach reaches one of the longest paths (2.5 km) reported in VO history with an high level of accuracy. [PERSON] et al. present two different systems that use an omnidirectional camera [20; 21]. The first uses an omnidirectional camera in a VO method to generate a reliable input for a mapping approach. The last presents several contributions, such as a strategy to deal with the scale uncertainty present in the monocular scheme and an epipolar constraint adapted to the omnidirectional geometry. Finally, [PERSON] et al. present a system that works with full-view omnidirectional images that are used in a spherical-model-based simultaneous localization and mapping (SLAM) approach in an indoor environment [22].