LUGOT: LiDAR and UWB Fusion for Global Given Object Tracking Under Mobile Anchors
The global trajectory of the leading vehicle in Global Navigation Satellite System (GNSS)-denied scenarios is important for vehicle formation. To achieve this, this article proposes light detection and ranging (LiDAR)-ultrawideband (UWB) global object tracking (LUGOT), which fuses LiDAR simultaneous localization and mapping (SLAM) and UWB for object tracking in a global coordinate system within mobile-anchor scenarios. First, based on LiDAR-SLAM, the global high-frequency pose of the following vehicle is obtained. This pose is then synchronized with the UWB ultrahigh-frequency timestamp using piecewise cubic Hermite interpolating polynomial (PCHIP). Next, the synchronized data are combined with UWB measurements. Finally, the combined data are filtered using the introduced extended Kalman filter (EKF)/unscented Kalman filter (UKF) with embedded outlier detection to determine the state of the object in the global coordinate system. To evaluate the filtering effect, this article introduces cubic smoothing spline fitting to smooth the raw global path of the object as the ground truth. The experimental fleet platform was built to evaluate the effectiveness, outlier detection, and filtering effect of LUGOT. It avoids the long-tail effect and can be quickly commercialized with current technology. The source code for LUGOT is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ly3106/LUGOT</uri>.
Authors
- Zheng Zang (ORCID: https://orcid.org/0000-0002-3782-7488)
- Yuanyuan Li (ORCID: https://orcid.org/0000-0002-2037-5286)
- Jie Fan (ORCID: https://orcid.org/0000-0003-4330-6053)
- Xingkun Li (ORCID: https://orcid.org/0009-0004-9987-0475)
- Wenjing Sun (ORCID: https://orcid.org/0000-0003-2096-5478)
- Jiahui Liu (ORCID: https://orcid.org/0009-0009-1477-6444)
- Xudong Zhang (ORCID: https://orcid.org/0000-0002-3943-1428)
- Yuan Zou (ORCID: https://orcid.org/0000-0003-2933-6168)
- Xiaoran Lu
Institutions
- Beijing Institute of Technology (CN)
- Tsinghua University (CN)
Publication Details
- Journal
- IEEE Sensors Journal
- Published
- 2024-11-27
- DOI
- https://doi.org/10.1109/jsen.2024.3503056
- Citations
- 3
- Primary Topic
- Indoor and Outdoor Localization Technologies
- Type
- article
- Field-Weighted Citation Impact
- 0.63
Funders
- National Natural Science Foundation of China