论文标题
基于欧几里得距离矩阵完成AGV定位在恶劣环境下的有效刚体定位
Efficient Rigid Body Localization based on Euclidean Distance Matrix Completion for AGV Positioning under Harsh Environment
论文作者
论文摘要
在实际的自动导航（AGV）导航的现实应用中，基于飞行时间（TOF）测量锚和标签之间的定位系统面临着由无线电信号或激光器等障碍物所引起的测量的问题。通过利用多个标签和锚点之间的测量值是一个刚体的定位（RBL）问题，它可以估计车辆的位置和态度。但是，RBL问题的最新解决方案并不涉及缺失的测量，因此在恶劣环境中会导致定位可用性和准确性降低。在本文中，与RBL的这些现有解决方案不同，我们将此问题建模为传感器网络本地化问题，缺少TOF。为了解决此问题，我们提出了一种基于欧几里得距离矩阵（EDM）完成的新的有效RBL解决方案，该解决方案缩写为ERBL-EDMC。首先，我们使用标签和TOF测量的统计数据之间的已知相对位置来确定丢失测量值的上和下限的方法，以可靠地完成EDM。然后，基于完整的EDM，从粗略估计中获得了全局标签位置，然后是对标记距离的改进步骤。最后，最佳车辆位置和态度是根据上一步的估计标签位置迭代获得的。理论分析和仿真结果表明，提出的ERBL-EDMC方法可以通过不完整的测量有效地解决了RBL问题。与基于半明确放松的现有RBL方法相比，它获得了最佳定位结果，同时保持低计算复杂性。
In real-world applications for automatic guided vehicle (AGV) navigation, the positioning system based on the time-of-flight (TOF) measurements between anchors and tags is confronted with the problem of insufficient measurements caused by blockages to radio signals or lasers, etc. Mounting multiple tags at different positions of the AGV to collect more TOFs is a feasible solution to tackle this difficulty. Vehicle localization by exploiting the measurements between multiple tags and anchors is a rigid body localization (RBL) problem, which estimates both the position and attitude of the vehicle. However, the state-of-the-art solutions to the RBL problem do not deal with missing measurements, and thus will result in degraded localization availability and accuracy in harsh environments. In this paper, different from these existing solutions for RBL, we model this problem as a sensor network localization problem with missing TOFs. To solve this problem, we propose a new efficient RBL solution based on Euclidean distance matrix (EDM) completion, abbreviated as ERBL-EDMC. Firstly, we develop a method to determine the upper and lower bounds of the missing measurements to complete the EDM reliably, using the known relative positions between tags and the statistics of the TOF measurements. Then, based on the completed EDM, the global tag positions are obtained from a coarse estimation followed by a refinement step assisted with inter-tag distances. Finally, the optimal vehicle position and attitude are obtained iteratively based on the estimated tag positions from the previous step. Theoretical analysis and simulation results show that the proposed ERBL-EDMC method effectively solves the RBL problem with incomplete measurements. It obtains the optimal positioning results while maintaining low computational complexity compared with the existing RBL methods based on semi-definite relaxation.
