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

By Albert Pumarola, Alexander Vakhitov, Antonio Agudo, Alberto Sanfeliu, Francesc Moreno-Noguer. IEEE International Conference on Robotics and Automation, 2017

460 citations recorded by the source corpus.

Low 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.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/233aaaba32065f8e16bada051e6d4dba73961762

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Citations in the corpus

PL-SLAM: A Stereo SLAM System Through the Combination of Points and Line Segments (2017, methodology): “Finally, by the time of the first submission of this paper, a work with the same name (PL-SLAM, [36]) was published extending the monocular algorithm ORB-SLAM to the case of including line segment features computed through the line segment detector (LSD) detector [37].”
PL-VIO: Tightly-Coupled Monocular Visual–Inertial Odometry Using Point and Line Features (2018, background): “For visual-only SLAM, there are several works combining point and line features to estimate camera motion [28,29].”