This paper presents a method of wide angle fovea visual odometry (WAF-VO) for Wide Angle Fovea Sensor SLAM (WAF-SLAM), by which a unique locally-high accurate and wide-angle map is generated in addition to camera motion estimation. The WAF sensor is a special-made wide-angle sensor that is inspired from human visual function, i.e., the spatial resolution of the image is not uniform throughout the entire field of view (FOV); it is much higher in the central FOV and decreases rapidly towards the periphery. Our visual odometry method is strongly characterized by a wide-angle FOV and space-variant resolution of the input image from the WAF sensor. A locally-high accurate and wide-angle mapping method is proposed as a major part for WAF-SLAM together with the camera motion estimation. Our proposed method estimates camera motions more stably using very low-spatial-resolution wide-angle images remapped from the input image of the WAF sensor. Using the estimated camera motions, narrow-angle high accurate maps are generated from corresponding feature points in high-spatial resolution central regions of the input image. Wide-angle maps are generated from ones in middle-spatial-resolution wide-angle images remapped from the input image apart from the above very low-spatial-resolution images. When the wide-angle maps are generated, the number of extracted feature points is increased by adjusting contrast threshold values of SIFT feature according to regions of the FOV. A KNN matching method improved using epipolar constraint is proposed and employed for avoidance of mismatching the increased feature points. Thus, the wide-angle maps are generated from more correct corresponding feature points. Finally, the above two types of maps are combined into the unique locally-high accurate and wide-angle map, i.e., a WAF map. Using our proposed method, the WAF map was generated by verification experiments. Furthermore, the paper presents an evaluation of the accuracy and precision of the generated map.
A Visual Odometry for Wide Angle Fovea Sensor SLAM
Carfi' A.;Mastrogiovanni F.
2021-01-01
Abstract
This paper presents a method of wide angle fovea visual odometry (WAF-VO) for Wide Angle Fovea Sensor SLAM (WAF-SLAM), by which a unique locally-high accurate and wide-angle map is generated in addition to camera motion estimation. The WAF sensor is a special-made wide-angle sensor that is inspired from human visual function, i.e., the spatial resolution of the image is not uniform throughout the entire field of view (FOV); it is much higher in the central FOV and decreases rapidly towards the periphery. Our visual odometry method is strongly characterized by a wide-angle FOV and space-variant resolution of the input image from the WAF sensor. A locally-high accurate and wide-angle mapping method is proposed as a major part for WAF-SLAM together with the camera motion estimation. Our proposed method estimates camera motions more stably using very low-spatial-resolution wide-angle images remapped from the input image of the WAF sensor. Using the estimated camera motions, narrow-angle high accurate maps are generated from corresponding feature points in high-spatial resolution central regions of the input image. Wide-angle maps are generated from ones in middle-spatial-resolution wide-angle images remapped from the input image apart from the above very low-spatial-resolution images. When the wide-angle maps are generated, the number of extracted feature points is increased by adjusting contrast threshold values of SIFT feature according to regions of the FOV. A KNN matching method improved using epipolar constraint is proposed and employed for avoidance of mismatching the increased feature points. Thus, the wide-angle maps are generated from more correct corresponding feature points. Finally, the above two types of maps are combined into the unique locally-high accurate and wide-angle map, i.e., a WAF map. Using our proposed method, the WAF map was generated by verification experiments. Furthermore, the paper presents an evaluation of the accuracy and precision of the generated map.
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