## 2 Literature Review  
In this section, we present the previous literature related to the visual odometry techniques of mobile robots, which employ image features on the surface of the ground plane with fisheye or wide-angle lens cameras. [PERSON] et al. [6] compare the visual localization performance of four well-known image features, which are extracted from fisheye images: Oriented FAST and Rotated BRIEF (ORB) [7], Speed-Up Robust Features (SURF) [8], Features from Accelerated Segment Test (FAST) [9], and KAZE [10]. They measure the translation and rotation errors of visual odometry computed from two different approaches: the standard visual odometry and the adaptive probability-oriented feature matching methods. A fisheye image dataset by [PERSON] et al. [11] is used for the experiment. The experimental results show that the performance of ORB and KAZE is relatively better than that of SURF and FAST. [PERSON] et al. [1] propose a novel visual SLAM (Simultaneous Localization and Mapping) method using cubemap images. They employ ORB-SLAM [12] as the baseline of their proposed CubemapSLAM method. Cubemap images are consecutively generated from fisheye images, and they are used as the input of ORB-SLAM. The cubemap camera model can use all image information of the fisheye camera without affecting the performance of feature descriptors. In this study, CubemapSLAM is efficiently implemented, and it can run in real time. Despite the limited angular resolution of the sensor, CubemapSLAM shows better accuracy in localization compared with the pinhole camera model [13]. [PERSON] [14] utilizes a single downward-pointing camera for the localization of a mobile robot. Image features are obtained from the camera, which is equipped with a wide-angle lens. The optical flow algorithm is employed to measure the location of the robot. In this study, the application of the proposed method was specifically targeted to robotic platforms in unseen areas. The author suggests applying the proposed method to any indoor or outdoor area when other localization sensors are not available. [PERSON] et al. [15] present a robust localization method based on monocular visual odometry. Accurate position estimation is presented even when a mobile robot is operated in undulating terrain. Their method employs a steering model to separately estimate the rotation and translation of the robot. Moreover, they also address the localization problem in an undulating terrain. The undulating terrain is approximated by multiple patches of locally flat surface. In each local flat surface, the inclination of the surface is estimated at the same time as the robot's motion. The surface inclination accuracy is about 1% error. Figure 2: The flow diagram of the proposed visual odometry method. [PERSON] et al. [16] present a visual odometry technique of a mobile robot that is working on a 2D plane by applying the kinematic model. A monocular camera facing downward is used to capture the images of the floor. The motion of the robot is estimated by the alignment between two consecutive images. The motion is then combined with the kinematic model of the robot to remove the outlier image regions that do not reflect the actual motion. To obtain the ortho-rectified floor images, they use a simple image homography method. [PERSON] et al. [17] address a ground plane-based odometry for the purpose of autonomous vehicle driving. They use a single normal vision camera to capture the ground image in front of a road-driving vehicle. To estimate the vehicle motion in real metric space, they propose a divide-and-conquer motion scale estimation method based on the ground plane and camera height. Their investigation is a typical example that the ground plane-based odometry can be used in the autonomous vehicle driving technique. [PERSON] et al. [18] introduce a ground plane-based visual odometry using a downward-facing RGBD camera. The RGBD camera captures the ground images and 3D point clouds frame by frame, and the images and points are transformed into a virtual orthogonal camera.