{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T08:07:00Z","timestamp":1773475620005,"version":"3.50.1"},"reference-count":22,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,24]],"date-time":"2021-10-24T00:00:00Z","timestamp":1635033600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Direct visual odometry algorithms assume that every frame from the camera has the same photometric characteristics. However, the cameras with auto exposure are widely used outdoors as the environment often changes. The vignetting also affects the pixel\u2019s brightness on different frames, even if the exposure time is fixed. We propose an online vignetting correction and exposure time estimation method for stereo direct visual odometry algorithms. Our method works on a camera that has a gamma-like response function. The inverse vignetting function and exposure time ratio between neighboring frames are estimated. Stereo matching is used to select correspondences between the left image and right image in the same frame at the initialization step. Feature points are used to pick the correspondences between different frames. Our method provides static correction results during the experiments on datasets and a stereo camera.<\/jats:p>","DOI":"10.3390\/s21217048","type":"journal-article","created":{"date-parts":[[2021,10,24]],"date-time":"2021-10-24T22:07:11Z","timestamp":1635113231000},"page":"7048","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Photometric Calibration for Stereo Camera with Gamma-like Response Function in Direct Visual Odometry"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5356-9044","authenticated-orcid":false,"given":"Yinming","family":"Miao","sequence":"first","affiliation":[{"name":"Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Yokohama 226-8503, Kanagawa, Japan"}]},{"given":"Masahiro","family":"Yamaguchi","sequence":"additional","affiliation":[{"name":"School of Science and Engineering, Tokyo Institute of Technology, Yokohama 226-8503, Kanagawa, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Engel, J., Sch\u00f6ps, T., and Cremers, D. (2014). LSD-SLAM: Large-scale direct monocular SLAM. Eur. Conf. Comput. Vis., 834\u2013849.","DOI":"10.1007\/978-3-319-10605-2_54"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1109\/TPAMI.2017.2658577","article-title":"Direct sparse odometry","volume":"40","author":"Engel","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Gao, X., Wang, R., Demmel, N., and Cremers, D. (2018, January 1\u20135). LDSO: Direct Sparse Odometry with Loop Closure. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8593376"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wang, R., Schworer, M., and Cremers, D. (2017, January 22\u201329). Stereo DSO: Large-scale direct sparse visual odometry with stereo cameras. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.421"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Mo, J., and Sattar, J. (2019, January 3\u20138). Extending Monocular Visual Odometry to Stereo Camera Systems by Scale Optimization. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.","DOI":"10.1109\/IROS40897.2019.8968272"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Debevec, P.E., and Malik, J. (2008). Recovering high dynamic range radiance maps from photographs. ACM SIGGRAPH 2008 Classes, ACM.","DOI":"10.1145\/1401132.1401174"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Asada, N., Amano, A., and Baba, M. (1996, January 25\u201329). Photometric calibration of zoom lens systems. Proceedings of the 13th International Conference on Pattern Recognition, Vienna, Austria.","DOI":"10.1109\/ICPR.1996.546016"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1364\/JOSAA.22.000839","article-title":"Radiometric framework for image mosaicking","volume":"22","author":"Litvinov","year":"2005","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1016\/j.cviu.2009.07.009","article-title":"Joint Radiometric Calibration and Feature Tracking for an Adaptive Stereo System","volume":"114","author":"Kim","year":"2010","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Grundmann, M., McClanahan, C., Kang, S.B., and Essa, I. (2013, January 19\u201321). Post-processing approach for radiometric self-calibration of video. Proceedings of the IEEE International Conference on Computational Photography (ICCP), Cambridge, MA, USA.","DOI":"10.1109\/ICCPhot.2013.6528307"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1109\/LRA.2017.2777002","article-title":"Online photometric calibration of auto exposure video for realtime visual odometry and slam","volume":"3","author":"Bergmann","year":"2017","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_12","unstructured":"Grossberg, M.D., and Nayar, S.K. (2003, January 18\u201320). What is the space of camera response functions?. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Madison, WI, USA."},{"key":"ref_13","unstructured":"Poynton, C. (2003). Digital Video and HDTV: Algorithms and Interfaces, Morgan Kaufmann."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1109\/TPAMI.2007.1166","article-title":"Stereo processing by semiglobal matching and mutual information","volume":"30","author":"Hirschmuller","year":"2007","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","article-title":"A computational approach to edge detection","volume":"6","author":"Canny","year":"1986","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1177\/0278364913491297","article-title":"Vision meets robotics: The kitti dataset","volume":"32","author":"Geiger","year":"2013","journal-title":"Int. J. Robot. Res."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. (2011, January 6\u201313). ORB: An efficient alternative to SIFT or SURF. Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"ref_18","unstructured":"Shi, X., Li, D., Zhao, P., Tian, Q., Tian, Y., Long, Q., Zhu, C., Song, J., Qiao, F., and Song, L. (August, January 31). Are We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM. Proceedings of the International Conference on Robotics and Automation (ICRA), Paris, France."},{"key":"ref_19","unstructured":"(2021, August 18). RealSense T265. Available online: https:\/\/www.intelrealsense.com\/tracking-camera-t265\/."},{"key":"ref_20","unstructured":"(2021, August 14). Leadsense Stereo Camera. Available online: http:\/\/leadsense.ilooktech.com\/product\/?lang=en."},{"key":"ref_21","unstructured":"(2021, August 14). YDLIDAR G4. Available online: https:\/\/www.ydlidar.com\/products\/view\/3.html."},{"key":"ref_22","unstructured":"(2021, August 14). TurtleBot2. Available online: https:\/\/www.turtlebot.com\/turtlebot2\/."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/7048\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:22:24Z","timestamp":1760167344000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/7048"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,24]]},"references-count":22,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["s21217048"],"URL":"https:\/\/doi.org\/10.3390\/s21217048","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,24]]}}}