<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Addressing the trade-off between operational efficiency and localization accuracy in visual SLAM, this paper introduces a monocular visual-inertial SLAM algorithm that integrates point and line features. To construct the point-line reprojection error and optimize the observation volume in front-end vision initialization, the motion recovery structure method (SFM) is employed through 3D reconstruction with a sliding window. The marginalization method uses the removed keyframe information as a priori constraint for nonlinear optimization in the back-end. In addition, the loopback detection algorithm is optimized in combination with the bag-of-words model and four-degree-of-freedom global bitmap to improve the accuracy of dynamic object detection, and the performance of the algorithm is tested. The results show that in the case of no closed loop, the absolute root mean square error of the algorithm proposed in this paper is lower than that of VINS-Mono (0.0625), PL-VIO (0.0401), and PL-VINS (0.0554) for the majority of sequences. In the case of closed loops, the absolute root mean square error of the proposed algorithm in this paper is reduced by 0.0395 and 0.0139 on average over most sequences compared to VINS-Mono and PL-VINS. The proposed algorithm in this paper demonstrates higher accuracy and robustness for improved detection and tracking of dynamic objects.</p>
</abstract>

Addressing the trade-off between operational efficiency and localization accuracy in visual SLAM, this paper introduces a monocular visual-inertial SLAM algorithm that integrates point and line features. To construct the point-line reprojection error and optimize the observation volume in front-end vision initialization, the motion recovery structure method (SFM) is employed through 3D reconstruction with a sliding window. The marginalization method uses the removed keyframe information as a priori constraint for nonlinear optimization in the back-end. In addition, the loopback detection algorithm is optimized in combination with the bag-of-words model and four-degree-of-freedom global bitmap to improve the accuracy of dynamic object detection, and the performance of the algorithm is tested. The results show that in the case of no closed loop, the absolute root mean square error of the algorithm proposed in this paper is lower than that of VINS-Mono (0.0625), PL-VIO (0.0401), and PL-VINS (0.0554) for the majority of sequences. In the case of closed loops, the absolute root mean square error of the proposed algorithm in this paper is reduced by 0.0395 and 0.0139 on average over most sequences compared to VINS-Mono and PL-VINS. The proposed algorithm in this paper demonstrates higher accuracy and robustness for improved detection and tracking of dynamic objects.

Dynamic Object Detection and Tracking in Vision SLAM

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Dynamic Object Detection and Tracking in Vision SLAM"}

Addressing the trade-off between operational efficiency and localization accuracy in visual SLAM, this paper introduces a monocular visual-inertial SLAM...

Dynamic Object Detection and Tracking in Vision SLAM

Published Online: May 22, 2024

Page range: -

Received: Feb 06, 2024

Accepted: Apr 10, 2024

DOI: https://doi.org/10.2478/amns-2024-1174

Keywords
Visual SLAM, Point-line features, SFM, Marginalization method, Loopback detection

© 2024 Heng Liu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Dynamic Object Detection and Tracking in Vision SLAM

Published Online: May 22, 2024

Page range: -

Received: Feb 06, 2024

Accepted: Apr 10, 2024

DOI: https://doi.org/10.2478/amns-2024-1174

KeywordsVisual SLAM, Point-line features, SFM, Marginalization method, Loopback detection

© 2024 Heng Liu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Visual SLAM, Point-line features, SFM, Marginalization method, Loopback detection