Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving
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DOI：
10.1109/tiv.2017.2749181
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发表时间：
2017-09-01
影响因子：
8.2
通讯作者：
Glaser, Sebastien
中科院分区：
文献类型：
--
作者：
Bresson, Guillaume;Alsayed, Zayed;Glaser, Sebastien
在本文中，我们提出了一个调查的同时定位和地图（SLAM）领域时，考虑到自动驾驶的最新发展。对自动驾驶汽车的兴趣日益增长，为定位和地图绘制技术提供了新的方向。在本调查中，我们首先概述了SLAM的不同分支，然后详细介绍了在考虑自主应用时感兴趣的特定趋势。我们首先介绍了自动驾驶的经典方法的局限性，并讨论了这种应用所必需的标准。然后，我们审查的方法，确定的挑战是解决。我们主要关注在各种条件下（天气，季节等）构建和重用长期地图的方法。我们还介绍了多车辆SLAM的新兴领域及其与自动驾驶汽车的联系。我们调查了该领域的不同范式（集中式和分布式）和现有的解决方案。最后，我们总结了迄今为止已经进行的各种大规模实验，并讨论了剩余的挑战和未来的方向。
In this paper, we propose a survey of the Simultaneous Localization And Mapping (SLAM) field when considering the recent evolution of autonomous driving. The growing interest regarding self-driving cars has given new directions to localization and mapping techniques. In this survey, we give an overview of the different branches of SLAM before going into the details of specific trends that are of interest when considered with autonomous applications in mind. We first present the limits of classical approaches for autonomous driving and discuss the criteria that are essential for this kind of application. We then review the methods where the identified challenges are tackled. We mostly focus on approaches building and reusing long-term maps in various conditions (weather, season, etc.). We also go through the emerging domain of multivehicle SLAM and its link with self-driving cars. We survey the different paradigms of that field (centralized and distributed) and the existing solutions. Finally, we conclude by giving an overview of the various large-scale experiments that have been carried out until now and discuss the remaining challenges and future orientations.