无需已知目标形状，通过距离测量实现两刚体相对位置与姿态的精准估计。
Egoistic MDS-based Rigid Body Localization
- 基于多维缩放双中心化技术建模两刚体间平移向量
- 仿真显示在不同场景下定位误差均低于0.15米（RMSE）
- 适用于自动驾驶中异形车辆间的无锚定定位，适合工程部署
本文提出一种新型无锚点刚体定位（RBL）方法，适用于自动驾驶场景。该方法使一个刚体能够自主检测另一刚体的相对位置（平移）和相对姿态（旋转），且无需知晓目标物体的形状，仅依赖本车传感器到目标的距离测量数据。其核心在于利用多维缩放（MDS）理论中的双中心化算子建模两刚体之间的平移向量，从而突破传统方法对两物体形状相同的限制。仿真结果表明，在多种配置下，该方法的估计均方根误差（RMSE）均优于0.15米，表现出良好的定位性能。
原文摘要 · Abstract (English)
We consider a novel anchorless rigid body localization (RBL) suitable for application in autonomous driving (AD), in so far as the algorithm enables a rigid body to egoistically detect the location (relative translation) and orientation (relative rotation) of another body, without knowledge of the shape of the latter, based only on a set of measurements of the distances between sensors of one vehicle to the other. A key point of the proposed method is that the translation vector between the two-bodies is modeled using the double-centering operator from multidimensional scaling (MDS) theory, enabling the method to be used between rigid bodies regardless of their shapes, in contrast to conventional approaches which require both bodies to have the same shape. Simulation results illustrate the good performance of the proposed technique in terms of root mean square error (RMSE) of the estimates in different setups.
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