TY - GEN
T1 - A Visual Odometry for Wide Angle Fovea Sensor SLAM
AU - Takamura, Tomoki
AU - Shimizu, Sota
AU - Murakami, Rei
AU - Carfi, Alessandro
AU - Mastrogiovanni, Fulvio
N1 - Funding Information:
This study was partially supported by JSPS Grants-in-Aid for Scientific Research No.18K04055 and No.21K03983.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/10/13
Y1 - 2021/10/13
N2 - 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.
AB - 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.
KW - WAF-SLAM
KW - WAF-VO
KW - high accuracy mapping
KW - space-variant image
KW - visual odometry
KW - wide angle fovea sensor
KW - wide-angle mapping
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U2 - 10.1109/IECON48115.2021.9589175
DO - 10.1109/IECON48115.2021.9589175
M3 - Conference contribution
AN - SCOPUS:85119472415
T3 - IECON Proceedings (Industrial Electronics Conference)
BT - IECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society
PB - IEEE Computer Society
T2 - 47th Annual Conference of the IEEE Industrial Electronics Society, IECON 2021
Y2 - 13 October 2021 through 16 October 2021
ER -