## 1 Introduction  
Nowadays, almost all robots utilize visual information for their tasks such as grasping, random bin picking, autonomous navigation, and so on. In these robotic applications, visual information, similar to human vision, provides plenty of important and useful information to robots. For this reason, more and more computational or artificial vision techniques have been developed. In particular, in the field of autonomous vehicles or mobile robots, obtaining visual information with a large field of view (FoV) is advantageous because it can reduce the blind areas around the vehicles or robots. In this regard, using the fisheye camera is an efficient way for a robot to obtain images with a large field of view. In addition, a small number of fisheye cameras can be used to obtain around-view images from the robot rather than using normal FoV cameras. Therefore, the fisheye camera is commonly used nowadays in many robotic applications. As one of the robotic applications, the vision-based or visual localization of a mobile robot is an essential technology in autonomous mapping and navigation. However, for certain types of robots, such as pallet or cleaning robots, it is inevitable that fisheye cameras are attached close to the ground level, as shown in Figure 1. Consequently, the ground area occupies half of the captured fisheye image. In this situation, vision-based localization is challenging mainly because of two reasons. First, half of image feature extraction and tracking is conducted in the ground area, while the texture pattern on the ground is generally uniform compared with other cluttered areas. Common feature extraction algorithms in computer vision are subject to extract a smaller number of features in the uniform ground area than the other surrounding areas. Thus, the ground area in a fisheye image is not cost effective in terms of feature extraction and tracking. Second, the undistortion of the fisheye image to a pinhole image causes inevitable image loss along the boundary of the original image. As a result, only the center area of the original image, where half of the center area is also the ground area, can be used in the undistorted pinhole image for feature extraction and tracking. Even though there are several disadvantages to using the fisheye camera in the low-profile robot, the ground information can be employed efficiently for visual navigation by assuming that the ground is a planar surface. Because almost all indoor mobile robots are assumed to move on the planar ground plane, the motion direction of a mobile robot can be considered parallel to the ground plane. This study is also motivated by the fact that the low-profile robot moves on a 2D planar space, which is parallel to the ground plane, and the fisheye camera captures the ground images at a very close distance to the ground. In this study, we propose using a visual odometry method with a fisheye camera for low-profile pallet robots. By only using the ground plane images generated from the fisheye camera, the pose of the robot is estimated. The proposed method consists of several steps, as shown in Figure 2. First, a fisheye image is converted to a cubemap image [1]. Second, a ground plane image (GPI) is obtained from the cubemap image. GPI is a virtual top-view image generated by the bottom and front faces of the cubemap. Third, a ground plane image is again converted to an ortho-rectified GPI to compensate for any distortion error due to lens distortion parameters. Fourth, image feature points are extracted and tracked using the optical flow in the ortho-rectified GPI. The robot motion is then modeled as a 2D vector in the image space [2]. Finally, as shown in Figure 2, the odometry of the robot is estimated by applying the bicycle kinematic model with the motion vector between consecutive image frames [3]. The architecture of the proposed visual odometry is named Ground Plane Odometry (GPO).