Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Bresson, Guillaume; Alsayed, Zayed; Li, Yu; Glaser, Sébastien

doi:10.1109/tiv.2017.2749181

Cited by 673 publications

(413 citation statements)

References 255 publications

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“…Localization is the task of finding ego-position relative to a reference frame in an environment [106], and it is fundamental to any mobile robot. It is especially crucial for ADSs [25]; the vehicle must use the correct lane and position [97], [105] to localize our vehicle in the Nagoya University campus. White points belong to the offline map and the colored ones were obtained from online scans.…”

Section: Localization and Mappingmentioning

confidence: 99%

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

et al. 2020

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Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high-level system architectures, emerging methodologies and core functions: localization, mapping, perception, planning, and human machine interface, were thoroughly reviewed. Furthermore, the state-of-the-art was implemented on our own platform and various algorithms were compared in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS

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Section: Localization and Mappingmentioning

confidence: 99%

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

et al. 2020

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“…These methods belong to the area of simultaneous localization and mapping (SLAM). For a survey on classical SLAM techniques, we refer the reader to Bresson, Alsayed, Yu, and Glaser ().…”

Section: Deep Learning For Driving Scene Perception and Localizationmentioning

confidence: 99%

“…Localization algorithms aim at calculating the pose (position and orientation) of the AV as it navigates. Although this can be achieved classical SLAM techniques, we refer the reader to Bresson, Alsayed, Yu, and Glaser (2017).…”

Section: Localizationmentioning

confidence: 99%

A survey of deep learning techniques for autonomous driving

Grigorescu

Trasnea

Cocias

et al. 2019

Journal of Field Robotics

1,127

495

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The last decade witnessed increasingly rapid progress in self‐driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence (AI). The objective of this paper is to survey the current state‐of‐the‐art on deep learning technologies used in autonomous driving. We start by presenting AI‐based self‐driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement learning paradigm. These methodologies form a base for the surveyed driving scene perception, path planning, behavior arbitration, and motion control algorithms. We investigate both the modular perception‐planning‐action pipeline, where each module is built using deep learning methods, as well as End2End systems, which directly map sensory information to steering commands. Additionally, we tackle current challenges encountered in designing AI architectures for autonomous driving, such as their safety, training data sources, and computational hardware. The comparison presented in this survey helps gain insight into the strengths and limitations of deep learning and AI approaches for autonomous driving and assist with design choices.

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“…However, such techniques induce intensive communication and computation requirements, and are not suited for building large geolocalized maps. Moreover, SLAM-based maps are often built with regard to the vehicle's reference, and can only be georeferenced through the use of GNSS sensors, again making the map's accuracy sensitive to GNSS weaknesses [3].…”

Section: State-of-the-artmentioning

confidence: 99%

A Collaborative Framework for High-Definition Mapping

Stoven-Dubois

Miguel

Dziri

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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For connected vehicles to have a substantial effect on road safety, it is required that accurate positions and trajectories can be shared. To this end, all vehicles must be accurately geo-localized in a common frame. This can be achieved by merging GNSS (Global Navigation Satellite System) information and visual observations matched with a map of geopositioned landmarks. Building such a map remains a challenge, and current solutions are facing strong cost-related limitations.We present a collaborative framework for high-definition mapping, in which vehicles equipped with standard sensors, such as a GNSS receiver and a mono-visual camera, update a map of geo-localized landmarks. Our system is composed of two processing blocks: the first one is embedded in each vehicle, and aims at geo-localizing the vehicle and the detected feature marks. The second is operated on cloud servers, and uses observations from all the vehicles to compute updates for the map of geo-positioned landmarks. As the map's landmarks are detected and positioned by more and more vehicles, the accuracy of the map increases, eventually converging in probability towards a null error. The landmarks' geo-positions are estimated in a stable and scalable way, enabling to provide dynamic map updates in an automatic manner.

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Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Cited by 673 publications

References 255 publications

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

A survey of deep learning techniques for autonomous driving

A Collaborative Framework for High-Definition Mapping

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