Abstract
Most visual–inertial navigation systems (VINSs) suffer from moving objects and achieve poor positioning accuracy in dynamic environments. Therefore, to improve the positioning accuracy of VINS in dynamic environments, a monocular visual–inertial navigation system, VINS-dimc, is proposed. This system integrates various constraints on the elimination of dynamic feature points, which helps to improve the positioning accuracy of VINSs in dynamic environments. First, the motion model, computed from the inertial measurement unit (IMU) data, is subjected to epipolar constraint and flow vector bound (FVB) constraint to eliminate feature matching that deviates significantly from the motion model. This algorithm then combines multiple feature point matching constraints that avoid the lack of single constraints and make the system more robust and universal. Finally, VINS-dimc was proposed, which can adapt to a dynamic environment. Experiments show that the proposed algorithm could accurately eliminate the dynamic feature points on moving objects while preserving the static feature points. It is a great help for the positioning accuracy and robustness of VINSs, whether they are from self-collected data or public datasets.
Highlights
Since only the file bag is moving in the scene, the ideal result is that there are no moving points on the file bag
The experiment shows that the proposed algorithm is still effective in the visual–inertial navigation systems (VINSs)
The proposed algorithm is suitable for public data sets. It can eliminate the feature points on the moving elevator while retaining other feature points, and there is no obvious error in feature matching
Summary
The best known VINS is VINSmono [9], which was proposed by Qin et al This system achieves accurate positioning of a device by observing visual feature points and pre-integrated IMU measurements. It can compute and calibrate extrinsic and temporal offsets between the camera and IMU online. An open platform named OpenVINS was proposed by Geneva et al [13] It uses some technologies, such as a sliding window Kalman filter, consistent First-Estimates Jacobian treatments and SLAM landmarks
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