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首页> 外文期刊>Journal of control, automation and electrical systems >Real-Time Autonomous Vehicle Localization Based on Particle and Unscented Kalman Filters
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Real-Time Autonomous Vehicle Localization Based on Particle and Unscented Kalman Filters

机译：基于粒子和智能的卡尔曼滤波器的实时自主车辆定位

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In this paper, a real-time Monte Carlo localization (RT_MCL) method for autonomous cars is proposed. Unlike the other localization approaches, the balanced treatment of both pose estimation accuracy and its real-time performance is the main contribution. The RT_MCL method is based on the fusion of lidar and radar measurement data for object detection, a pole-like landmarks probabilistic map and a tailored particle filter for pose estimation. The lidar and radar are fused using the unscented Kalman filter (UKF) to provide pole-like static-object pose estimations that are well suited to serve as landmarks for vehicle localization in urban environments. These pose estimations are then clustered using the grid-based density-based spatial clustering of applications with noise algorithm to represent each pole landmark in the form of a source-point model to reduce computational cost and memory requirements. A reference map that includes pole landmarks is generated offline and extracted from a 3-D lidar to be used by a carefully designed particle filter for accurate ego-car localization. The particle filter is initialized by the fused GPS?+?IMU measurements and used an ego-car motion model to predict the states of the particles. The data association between the estimated landmarks by the UKF and that in the reference map is performed using the iterative closest point algorithm. The RT_MCL is implemented using the high-performance language C++? and utilizes highly optimized math and optimization libraries for best real-time performance. Extensive simulation studies have been carried out to evaluate the performance of the RT_MCL in both longitudinal localization and lateral localization.
机译：在本文中，提出了一种自主车的实时蒙特卡罗定位（RT_MCL）方法。与其他本地化方法不同，对姿势估计准确性的平衡处理及其实时性能是主要贡献。 RT_MCL方法基于LIDAR和雷达测量数据的融合，用于对象检测，杆状地标概率图和用于姿势估计的定制粒子滤波器。 LIDAR和RADAR使用UNSCENTED的卡尔曼滤波器（UKF）融合，以提供极性的静态对象姿态估计，其非常适合用作城市环境中车辆本地化的地标。然后使用具有噪声算法的基于网格的密度的空间聚类来聚集这些姿势估计，以源点模型的形式代表每个极杆地标，以降低计算成本和存储器要求。包括极值地标的参考图是离线产生的，并从三维激光器中提取，以便精心设计的粒子过滤器用于精确的自助式本地化。颗粒滤波器被融合的GPS初始化？IMU测量并使用了EGO-CAR运动模型来预测颗粒的状态。 UKF估计的地标之间的数据关联以及在参考图中使用迭代最近点算法来执行。使用高性能语言C ++实现RT_MCL？并利用高度优化的数学和优化库进行最佳实时性能。已经进行了广泛的仿真研究，以评估RT_MCL在纵向定位和横向定位中的性能。

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