Abstract
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.
Highlights
Localization and navigation have attracted much attention in recent years with respect to a wide range of applications, for self-driving cars, service robots, and unmanned aerial vehicles, etc
They have obvious respective drawbacks: global navigation satellite systems (GNSSs) only provide reliable localization information if there is a clear sky view [6]; laser lidar suffers from a reflection problem for objects with glass surfaces [7]; measurements from civilian inertial measurement units (IMUs) are noisy, such that inertial navigation systems may drift quickly due to error accumulation [8]; and monocular simultaneous localization and mapping (SLAM) can only recover the motion trajectory up to a certain scale and it tends to be lost when the camera moves fast or illumination changes dramatically [9,10,11]
We evaluated our point–line visual–inertial odometry (PL-visual–inertial odometry (VIO)) system using two public benchmark datasets: the EuRoc micro aerial vehicle (MAV)
Summary
Localization and navigation have attracted much attention in recent years with respect to a wide range of applications, for self-driving cars, service robots, and unmanned aerial vehicles, etc. Optimization-based approaches can repeat the linearization of a state vector at different points to achieve higher accuracy than filtering-based methods [14]. Kong et al [25] built a stereo VIO system combining point and line features by utilizing trifocal geometry In our proposed PL-VIO method, we integrate line features into the optimization framework in order achieve higher accuracy than filtering-based methods. To build a structural 3D map and obtain the camera’s motion, we propose the PL-VIO system, which optimizes the system states by jointly minimizing the IMU pre-integration constraints together with the point and line re-projection errors in sliding windows. To tightly and efficiently fuse the information from visual and inertial sensors, we introduce a sliding window model with IMU pre-integration constraints and point/line features.
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