利用基于概率图和非增益卡尔曼滤波的传感器融合技术进行实时蒙特卡洛定位

IF 2.6 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Wael A. Farag, Julien Moussa H. Barakat
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引用次数: 0

摘要

自动驾驶汽车必须高精度地知道自己的位置,才能在城市和高速公路环境中安全可靠地行驶。因此,本文提出并实现了一种用于自动驾驶汽车的可靠且相对精确的位置估计(定位)技术。在处理获得的感知数据或给定的车辆周围环境知识时,所提出的方法采用了概率方法。在这种方法中,所涉及的概率密度是通过保留从中随机选择的样本集合(蒙特卡罗模拟)来表示的。因此,这种蒙特卡罗采样允许用任何任意分布来表示位置估计结果,而不仅仅是高斯分布。为实现这种基于蒙特卡洛的定位,所选择的技术是基于粒子密度表示的贝叶斯滤波(即粒子滤波器)。所采用的粒子滤波器从经过仔细调整的无香料卡尔曼滤波器(UKF)接收周围物体的范围,该滤波器用于融合雷达和激光雷达的感测读数。感官读数用于检测电子定位仪周围的杆状静态物体,并将其与提供的详细参考地图中的物体进行比较,参考地图中包含离线生成并从三维激光雷达扫描中提取的杆状地标。为了评估所提出的技术在横向和纵向定位方面的效果,我们进行了全面的模拟测试。结果表明,所提出的技术在横向和纵向平均位置误差较小方面优于其他技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Utilizing Probabilistic Maps and Unscented-Kalman-Filtering-Based Sensor Fusion for Real-Time Monte Carlo Localization
An autonomous car must know where it is with high precision in order to maneuver safely and reliably in both urban and highway environments. Thus, in this paper, a reliable and relatively precise position estimation (localization) technique for autonomous vehicles is proposed and implemented. In dealing with the obtained sensory data or given knowledge about the vehicle’s surroundings, the proposed method takes a probabilistic approach. In this approach, the involved probability densities are expressed by keeping a collection of samples selected at random from them (Monte Carlo simulation). Consequently, this Monte Carlo sampling allows the resultant position estimates to be represented with any arbitrary distribution, not only a Gaussian one. The selected technique to implement this Monte-Carlo-based localization is Bayesian filtering with particle-based density representations (i.e., particle filters). The employed particle filter receives the surrounding object ranges from a carefully tuned Unscented Kalman Filter (UKF) that is used to fuse radar and lidar sensory readings. The sensory readings are used to detect pole-like static objects in the egocar’s surroundings and compare them to the ones that exist in a supplied detailed reference map that contains pole-like landmarks that are produced offline and extracted from a 3D lidar scan. Comprehensive simulation tests were conducted to evaluate the outcome of the proposed technique in both lateral and longitudinal localization. The results show that the proposed technique outperforms the other techniques in terms of smaller lateral and longitudinal mean position errors.
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来源期刊
World Electric Vehicle Journal
World Electric Vehicle Journal Engineering-Automotive Engineering
CiteScore
4.50
自引率
8.70%
发文量
196
审稿时长
8 weeks
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