PL VIO: Tightly Coupled Monocular Visual Inertial Odometry Using Point and Line Features
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, Plucker 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.
Sensors