TY - JOUR
T1 - LUGOT
T2 - LiDAR and UWB Fusion for Global Given Object Tracking Under Mobile Anchors
AU - Li, Yuanyuan
AU - Zou, Yuan
AU - Zhang, Xudong
AU - Zang, Zheng
AU - Sun, Wenjing
AU - Fan, Jie
AU - Lu, Xiaoran
AU - Li, Xingkun
AU - Liu, Jiahui
N1 - Publisher Copyright:
© 2001-2012 IEEE.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - Extended Kalman filter (EKF)
KW - global object tracking
KW - light detection and ranging (LiDAR)
KW - simultaneous localization and mapping (SLAM)
KW - ultrawideband (UWB)
KW - unscented Kalman Filter (UKF)
UR - http://www.scopus.com/inward/record.url?scp=85210769536&partnerID=8YFLogxK
U2 - 10.1109/JSEN.2024.3503056
DO - 10.1109/JSEN.2024.3503056
M3 - Article
AN - SCOPUS:85210769536
SN - 1530-437X
VL - 25
SP - 4882
EP - 4896
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 3
ER -