This paper develops a distributed collaborative localization algorithm based on an extended kalman filter. This algorithm incorporates Ultra-Wideband (UWB) measurements for vehicle to vehicle ranging, and shows improvements in localization accuracy where GPS typically falls short. The algorithm was first tested in a newly created open-source simulation environment that emulates various numbers of vehicles and sensors while simultaneously testing multiple localization algorithms. Predicted error distributions for various algorithms are quickly producible using the Monte-Carlo method and optimization techniques within MatLab. The simulation results were validated experimentally in an outdoor, urban environment. Improvements of localization accuracy over a typical extended kalman filter ranged from 2.9% to 9.3% over 180 meter test runs. When GPS was denied, these improvements increased up to 83.3% over a standard kalman filter. In both simulation and experimentally, the DCL algorithm was shown to be a good approximation of a full state filter, while reducing required communication between vehicles. These results are promising in showing the efficacy of adding UWB ranging sensors to cars for collaborative and landmark localization, especially in GPS-denied environments. In the future, additional moving vehicles with additional tags will be tested in other challenging GPS denied environments.

中文翻译：

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车辆团队合作：自动驾驶汽车的协作本地化

本文提出了一种基于扩展卡尔曼滤波器的分布式协同定位算法。该算法结合了用于车辆到车辆测距的超宽带（UWB）测量，并显示了GPS通常不足的定位精度的提高。该算法首先在新创建的开源仿真环境中进行了测试，该仿真环境可以模拟各种数量的车辆和传感器，同时可以测试多种定位算法。使用蒙特卡洛方法和MatLab中的优化技术，可以快速生成各种算法的预测误差分布。仿真结果在室外的城市环境中进行了实验验证。在180米的测试运行中，典型的扩展卡尔曼滤波器的定位精度提高了2.9％至9.3％。当GPS被拒绝时，这些改进比标准的卡尔曼滤波器提高了83.3％。在仿真和实验中，DCL算法都被证明是全状态滤波器的良好近似，同时减少了车辆之间所需的通信。这些结果有望显示出将UWB测距传感器添加到汽车中以进行协作和地标定位的功效，特别是在GPS受限的环境中。将来，带有其他标签的其他移动车辆将在其他具有挑战性的GPS拒绝环境中进行测试。这些结果有望显示出将UWB测距传感器添加到汽车中以进行协作和地标定位的功效，特别是在GPS受限的环境中。将来，带有其他标签的其他移动车辆将在其他具有挑战性的GPS拒绝环境中进行测试。这些结果有望显示出将UWB测距传感器添加到汽车中以进行协作和地标定位的功效，特别是在GPS受限的环境中。将来，带有其他标签的其他移动车辆将在其他具有挑战性的GPS拒绝环境中进行测试。