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Multi-Sensor Fusion for Navigation and Mapping in Autonomous Vehicles: Accurate Localization in Urban Environments

机译：自动车辆中导航和映射的多传感器融合：城市环境中的准确本地化

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摘要

The combination of data from multiple sensors, also known as sensor fusion or data fusion, is a key aspect in the design of autonomous robots. In particular, algorithms able to accommodate sensor fusion techniques enable increased accuracy, and are more resilient against the malfunction of individual sensors. The development of algorithms for autonomous navigation, mapping and localization have seen big advancements over the past two decades. Nonetheless, challenges remain in developing robust solutions for accurate localization in dense urban environments, where the so-called last-mile delivery occurs. In these scenarios, local motion estimation is combined with the matching of real-time data with a detailed pre-built map. In this paper, we utilize data gathered with an autonomous delivery robot to compare different sensor fusion techniques and evaluate which are the algorithms providing the highest accuracy depending on the environment. The techniques we analyze and propose in this paper utilize 3D lidar data, inertial data, GNSS data and wheel encoder readings. We show how lidar scan matching combined with other sensor data can be used to increase the accuracy of the robot localization and, in consequence, its navigation. Moreover, we propose a strategy to reduce the impact on navigation performance when a change in the environment renders map data invalid or part of the available map is corrupted.
机译：来自多个传感器的数据组合，也称为传感器融合或数据融合，是自主机器人设计中的一个关键方面。特别地，能够容纳传感器融合技术的算法使得能够提高精度，并且对单个传感器的故障更具弹性。自主导航，映射和本地化算法的开发在过去二十年中看到了大的进步。尽管如此，挑战仍然是制定强大的城市环境中准确本地化的强大解决方案，其中发生了所谓的最后一次交货。在这些场景中，本地运动估计与具有详细预构建地图的实时数据的匹配组合。在本文中，我们利用了与自主输送机器人聚集的数据进行比较不同的传感器融合技术和评估，这是根据环境提供最高精度的算法。我们在本文中分析和提出的技术利用3D LIDAR数据，惯性数据，GNSS数据和轮编码器读数。我们展示LIDAR扫描匹配如何与其他传感器数据相结合，可用于提高机器人本地化的准确性，结果是其导航。此外，我们提出了一种策略，以减少对导航性能的影响，当环境渲染的变化映射数据无效或可用地图的一部分损坏时。

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