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PL-SLAM: Real-time monocular visual SLAM with points and lines

AutorAgudo, Antonio ; Sanfeliu, Alberto; Moreno-Noguer, Francesc
Fecha de publicación2017
EditorInstitute of Electrical and Electronics Engineers
CitaciónIEEE International Conference on Robotics and Automation: 4503-4508 (2017)
ResumenLow textured scenes are well known to be one of the main Achilles heels of geometric computer vision algorithms relying on point correspondences, and in particular for visual SLAM. Yet, there are many environments in which, despite being low textured, one can still reliably estimate line-based geometric primitives, for instance in city and indoor scenes, or in the so-called ``Manhattan worlds'', where structured edges are predominant. In this paper we propose a solution to handle these situations. Specifically, we build upon ORB-SLAM, presumably the current state-of-the-art solution both in terms of accuracy as efficiency, and extend its formulation to simultaneously handle both point and line correspondences. We propose a solution that can even work when most of the points are vanished out from the input images, and, interestingly it can be initialized from solely the detection of line correspondences in three consecutive frames. We thoroughly evaluate our approach and the new initialization strategy on the TUM RGB-D benchmark and demonstrate that the use of lines does not only improve the performance of the original ORB-SLAM solution in poorly textured frames, but also systematically improves it in sequence frames combining points and lines, without compromising the efficiency.
DescripciónTrabajo presentado a la IEEE International Conference on Robotics and Automation (ICRA), celebrada en Singapur del 29 de mayo al 3 de junio de 2017.
Versión del editorhttps://doi.org/10.1109/ICRA.2017.7989522
Identificadoresdoi: 10.1109/ICRA.2017.7989522
isbn: 978-1-5090-4634-8
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