PL-VIO: Tightly-Coupled Monocular Visual–Inertial Odometry Using Point and Line Features
By Yijia He, Ji Zhao, Yue Guo, Wenhao He, Kui Yuan. Italian National Conference on Sensors, 2018
304 citations recorded by the source corpus.
To address the problem of estimating camera trajectory and to build a structural three-dimensional (3D) map based on inertial measurements and visual observations, this paper proposes point–line visual–inertial odometry (PL-VIO), a tightly-coupled monocular visual–inertial odometry system exploiting both point and line features. Compared with point features, lines provide significantly more geometrical structure information on the environment. To obtain both computation simplicity and representational compactness of a 3D spatial line, Plücker coordinates and orthonormal representation for the line are employed. To tightly and efficiently fuse the information from inertial measurement units (IMUs) and visual sensors, we optimize the states by minimizing a cost function which combines the pre-integrated IMU error term together with the point and line re-projection error terms in a sliding window optimization framework. The experiments evaluated on public datasets demonstrate that the PL-VIO method that combines point and line features outperforms several state-of-the-art VIO systems which use point features only.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Geology
View Paper: https://www.semanticscholar.org/paper/6356897ee4a51a1f9de04a8ab959afd97af7f63e
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Citations in the corpus
- Visual-Inertial Navigation: A Concise Review (2019, methodology): “On the other hand, in the EKF framework, different geometric features besides points have also been exploited to improve VINS performance, for example, line features used in [64, 65, 66, 67, 68] and plane features in [69, 70, 71, 72].”