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<\/figure>Rub\u00e9n G\u00f3mez Ojeda<\/strong> Welcome!!<\/p>\n\n\n\n I am Rub\u00e9n G\u00f3mez Ojeda, a PhD student in the Machine Perception and Intelligent Robotics group (MAPIR) at the University of M\u00e1laga (Spain). My research interests include:<\/p>\n\n\n\n Contents<\/p> I was born in M\u00e1laga (Spain) in 1988. I received a B.Sc-M.Sc in “Ingenier\u00eda Industrial” (a general engineering which covers mechanics, electricity, electronic, computer science, etc.) from the University of M\u00e1laga in 2012. In October 2013 I joined the Machine Perception and Intelligent Robotics (MAPIR)<\/a> group as a Researcher in Computer Vision. I received the M.Sc in Mechatronics in 2014. I received a grant (DPI2014-55826-R) from the National (Spanish) Plan of Research to do a 4-year PhD under the supervision of Prof. Javier Gonz\u00e1lez-Jim\u00e9nez<\/a>, which I started in November 2015. From October 2016 to February 2017 I was a visting researcher at the Robotics and Perception Group of the University of Zurich<\/a>, under the supervision of Prof. Davide Scaramuzza<\/a>. From June to September 2018 I was a research intern at Oculus in Zurich.<\/p>\n\n\n\n In this work, we propose a purely geometrical approach for the robust matching of line segments for challenging stereo streams with severe illumination changes or High Dynamic Range (HDR) environments. To that purpose, we exploit the univocal nature of the matching problem, i.e. every observation must be corresponded with a single feature or not corresponded at all. We state the problem as a sparse, convex, L1-minimization of the matching vector regularized by the geometric constraints. This formulation allows for the robust tracking of line segments along sequences where traditional appearance-based matching techniques tend to fail due to dynamic changes in illumination conditions. Moreover, the proposed matching algorithm also results in a considerable speed-up of previous state of the art techniques making it suitable for real-time applications such as Visual Odometry (VO). This, of course, comes at expense of a slightly lower number of matches in comparison with appearance based methods, and also limits its application to continuous video sequences, as it is rather constrained to small pose increments between consecutive frames.We validate the claimed advantages by first evaluating the matching performance in challenging video sequences, and then testing the method in a benchmarked point and line based VO algorithm.<\/p>\n\n\n\n Arxiv draft: https:\/\/arxiv.org\/abs\/1809.09368<\/a><\/p>\n\n\n\n
PhD. student in Computer Vision and Mobile Robotics<\/p>\n<\/div><\/div>\n\n\n\n![\"\"](\"https:\/\/mapir.isa.uma.es\/mapirwebsite\/wp-content\/uploads\/2021\/12\/GoogleScholar.png\")
<\/a><\/figure><\/div>\n<\/div><\/div>\n\n\n\nBrief CV<\/strong><\/span><\/h2>\n\n\n\n
Research<\/strong><\/span><\/h2>\n\n\n\n
Geometric-based Line Segment Tracking for HDR Stereo Sequences<\/strong><\/strong><\/span><\/h3>\n\n\n\n