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Fast real-time localization with sparse digital maps for connected automated vehicles in urban areas

机译：稀疏数字地图的快速实时本地化，适用于市区内联网的自动驾驶汽车

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摘要

For the realization of advanced automation systems in road vehicles, accurate and robust localization is a crucial requirement. Satellite-based systems such as GPS are generally able to provide geolocations, but their precision and robustness can be impaired strongly due to shading of satellites and multipath effects, especially in urban surroundings. Localization methods based on environment perception sensors and digital maps are therefore widely used in the field of autonomous vehicles with accuracy, robustness, real-time capability and sparseness of the maps being major objectives. In this paper, we present a fast, real-time capable implementation of a Monte Carlo Localization scheme which operates on a storage space efficient digital map and is targeted to provide precise localization in urban surroundings at a rate of 50 Hz. For the experimental evaluation, we use our test vehicle’s LiDAR sensor combined with wheel odometry, inertial measurements and a low-cost GPS.
机译：为了在公路车辆中实现先进的自动化系统，准确而强大的本地化是至关重要的要求。基于卫星的系统（例如GPS）通常能够提供地理位置信息，但是由于卫星的阴影和多径效应（尤其是在城市环境中），其精度和鲁棒性会大大受损。因此，基于环境感知传感器和数字地图的定位方法被广泛应用于自动驾驶汽车领域，其准确性，鲁棒性，实时性和稀疏性是主要目标。在本文中，我们提出了一种可在存储空间高效的数字地图上运行的蒙特卡洛定位方案的快速，实时功能，该方案旨在以50 Hz的频率在城市环境中提供精确的定位。为了进行实验评估，我们将测试车辆的LiDAR传感器与车轮里程计，惯性测量和低成本GPS结合使用。

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