Version 1
: Received: 6 December 2023 / Approved: 6 December 2023 / Online: 6 December 2023 (11:03:51 CET)
How to cite:
Huang, J.; Xia, Q.; Kang, L. TKG-SLAM：Dynamic SLAM Based on Target Tracking and Multi-View Geometry. Preprints2023, 2023120406. https://doi.org/10.20944/preprints202312.0406.v1
Huang, J.; Xia, Q.; Kang, L. TKG-SLAM：Dynamic SLAM Based on Target Tracking and Multi-View Geometry. Preprints 2023, 2023120406. https://doi.org/10.20944/preprints202312.0406.v1
Huang, J.; Xia, Q.; Kang, L. TKG-SLAM：Dynamic SLAM Based on Target Tracking and Multi-View Geometry. Preprints2023, 2023120406. https://doi.org/10.20944/preprints202312.0406.v1
APA Style
Huang, J., Xia, Q., & Kang, L. (2023). TKG-SLAM：Dynamic SLAM Based on Target Tracking and Multi-View Geometry. Preprints. https://doi.org/10.20944/preprints202312.0406.v1
Chicago/Turabian Style
Huang, J., QiMeng Xia and Li Kang. 2023 "TKG-SLAM：Dynamic SLAM Based on Target Tracking and Multi-View Geometry" Preprints. https://doi.org/10.20944/preprints202312.0406.v1
Abstract
The dynamic factors in the environment violate the static environment assumption of the SLAM algorithm, leading to a decrease in the accuracy of camera positioning. In recent years, many studies have attempted to address dynamic objects by combining semantic constraints and geometric constraints, but issues such as poor real-time performance, the potential for mistakenly treating humans as rigid objects, and subpar performance in high-dynamic scenes still persist. This paper proposes a dynamic scene visual SLAM algorithm called Dynamic SLAM based on target tracking and multi-view geometry (TKG-SLAM), based on object detection, Kalman filters, and multi-view geometry. This algorithm takes into consideration both real-time performance and algorithm accuracy. It combines semantic constraints and multi-view geometry constraints, selectively running the algorithm in different scenarios, filtering and preserving static points for camera pose estimation. Experimental results demonstrate that, compared to current state-of-the-art dynamic SLAM methods, our approach achieves optimal performance in some scenarios and exhibits stronger real-time capabilities.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
