To navigate autonomously, a vehicle must be able to localize itself with respect to its driving environment and the vehicles with which it interacts. This study presents a decentralized cooperative localization method. It is based on the exchange of local dynamic maps (LDM), which are cyber-physical representations of the physical driving environment containing poses and kinematic information about nearby vehicles. An LDM acts as an abstraction layer that makes the cooperation framework sensor-agnostic, and it can even improve the localization of a sensorless communicating vehicle. With this goal in mind, this study focuses on the property of consistency in LDM estimates. Uncertainty in the estimates needs to be properly modeled, so that the estimation error can be statistically bounded for a given confidence level. To obtain a consistent system, we first introduce a decentralized fusion framework that can cope with LDMs whose errors have an unknown degree of correlation. Second, we present a consistent method for estimating the relative pose between vehicles, using a two-dimensional LiDAR (light detection and ranging) with a point-to-line metric within an iterative-closest-point approach, combined with communicated polygonal shape models. Finally, we add a bias estimator to reduce position errors when nondifferential GNSS (global navigation satellite system) receivers are used, based on visual observations of features geo-referenced in a high-definition map. Real experiments were conducted, and the consistency of our approach was demonstrated on a platooning scenario using two experimental vehicles. The full experimental data set used in this study is publicly available.

中文翻译：

---

使用LiDAR，GNSS和HD地图对自动驾驶汽车进行分散的合作式协作一致定位

为了自主导航，车辆必须能够相对于其驾驶环境以及与之交互的车辆定位自身。本研究提出了一种分散式合作定位方法。它基于本地动态地图（LDM）的交换，本地动态地图是物理驾驶环境的网络物理表示，其中包含有关附近车辆的姿态和运动学信息。LDM充当使协作框架不可知的抽象层，甚至可以改善无传感器通信车辆的定位。考虑到这一目标，本研究着重于LDM估计中一致性的属性。估计中的不确定性需要适当地建模，以便对于给定的置信度，可以将估计误差统计上界定。为了获得一致的系统，我们首先介绍一种分散式融合框架，该框架可以处理错误相关程度未知的LDM。其次，我们提出了一种一致的方法，用于估计车辆之间的相对姿态，该方法使用二维LiDAR（光检测和测距）以及迭代最接近点方法中的点对线度量，并与通信的多边形形状模型结合使用。最后，基于对高清地图中地理参考要素的视觉观察，我们添加了一个偏差估计器，以减少使用非差分GNSS（全球导航卫星系统）接收器时的位置误差。进行了真实的实验，并使用两个实验工具在一个排成一行的场景中证明了我们方法的一致性。这项研究中使用的完整实验数据集是公开可用的。