###### Abstract  
This paper presents a novel self-localization technique for mobile robots based on image feature matching from omnidirectional vision. The proposed method first constructs a virtual space with synthetic omnidirectional imaging to simulate a mobile robot equipped with an omnidirectional vision system in the real world. In the virtual space, a number of vertical and horizontal lines are generated according to the structure of the environment. They are imaged by the virtual omnidirectional camera using the catadoptric projection model. The omnidirectional images derived from the virtual and real environments are then used to match the synthetic lines and real scene edges. Finally, the pose and trajectory of the mobile robot in the real world are estimated by the efficient perspective-n-point (EPnP) algorithm based on the line feature matching. In our experiments, the effectiveness of the proposed self-localization technique was validated by the navigation of a mobile robot in a real world environment. mobile robot localization; robot vision; omnidirectional imaging system +
Footnote †: journal: Applied  
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Footnote 0: _Appl. The other self-localization approach is based on the data registration of the 3D models reconstructed from the real scene environment at different locations. [PERSON] proposed a technique using the iterative closest point (ICP) algorithm to compute the robot's movement [13]. The 3D registration is carried out on two sets of sensor data obtained from sonar, a laser rangefinder or an RGB-D camera. Due to its high computational cost and insufficient accuracy, a number of works have been proposed for further improvements [14; 15]. In addition, [PERSON] et al. compared the performance of various registration techniques, including the 3D data obtained from multiple viewpoints [16]. In the past few decades, omnidirectional cameras have been widely adopted to the mobile robot applications due to their capability with observing the panoramic viewpoints of the surroundings [17]. Traditional studies attempted to identify the features of the environment (edges, points or lines) by direct extraction from the images captured by the camera. For mobile robot localization, [PERSON] et al. presented a beacon-based technique using an omnidirectional camera [18]. The image features are extracted using SURF with the scale and orientation invariant. [PERSON] et al. proposed a localization method based on the parallel lines appearing in omnidirectional images [19]. The line features in the structured environment are detected using structure-from-motion. There also exist various stereo vision-based approaches using multiple cameras [20]. Nevertheless, the direct use of image information brings disadvantages for robot self-localization, for example, the problems associated with illumination change, motion blur, etc. In this paper, we present a novel self-localization approach using the generated virtual environment to simulate the robot navigation in the real world. The basic idea is to calculate the localization position through the computer generated virtual environment. In the simulated virtual space, a mobile robot equipped with an omnidirectional vision sensor represents a mirror of its real world counterpart. Since computing the 3D position without the illumination change and noise in the simulated environment is much easier, it is used to process the image features extracted from the real world scenes. In the existing literature, the omnidirectional images are used to directly deal with the objects without the virtual information. This work suggests creating a parallel virtual space which simulates the environment in the real world. A mobile robot navigating along a trajectory is used to evaluate the proposed localization technique. This paper is organized as follows. In the next section, we introduce the omnidirectional vision system adopted in this work and the catadioptric image projection model.