2026-05-30T08:13:44Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/143762026-02-03T12:00:10Zcom_10630_2254col_10630_37959

Accurate Stereo Visual Odometry with Gamma Distributions Gómez-Ojeda, Rubén Moreno-Dueñas, Francisco Ángel González-Jiménez, Antonio Javier Campo visual visual odometry SLAM Stereo vision Point-based stereo visual odometry systems typically estimate the camera motion by minimizing a cost function of the projection residuals between consecutive frames. Under some mild assumptions, such minimization is equivalent to maximizing the probability of the measured residuals given a certain pose change, for which a suitable model of the error distribution (sensor model) becomes of capital importance in order to obtain accurate results. This paper proposes a robust probabilistic model for projection errors, based on real world data. For that, we argue that projection distances follow Gamma distributions, and hence, the introduction of these models in a probabilistic formulation of the motion estimation process increases both precision and accuracy. Our approach has been validated through a series of experiments with both synthetic and real data, revealing an improvement in accuracy while not increasing the computational burden. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Project "PROMOVE: Advances in mobile robotics for promoting independent life of elders", funded by the Spanish Government and the "European Regional Development Fund ERDF" under contract DPI2014-55826-R. 2017-07-26T07:33:02Z 2017-07-26T07:33:02Z 2017 2017 conference output http://hdl.handle.net/10630/14376 http://orcid.org/0000-0003-3845-3497 eng IEEE International Conference on Robotics and Automation (ICRA) Singapore May, 2017 by-nc-nd open access application/pdf IEEE