Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving
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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.
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Localization is especially crucial for ADSs; the vehicle must use the correct lane and position itself in it accurately.
“It is especially crucial for ADSs [21]; the vehicle must use the correct lane and position itself in it accurately.”
From A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i> (2020) · cites this paper as [21]
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A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i>
Yurtsever, E., Lambert, J., Carballo, A., & Takeda, K. (2020) · IEEE Access · doi:10.1109/access.2020.2983149 · Cited by 1,889 · see citing works
“Like in aircraft, ADSs need to be designed with high redundancies that will minimize the chance of a catastrophic failure.”
BackgroundMethodInfluentialA survey of deep learning techniques for autonomous driving
Grigorescu, S., Trasnea, B., Cocias, T., & Macesanu, G. (2020) · Journal of Field Robotics · doi:10.1002/rob.21918 · Cited by 1,809 · see citing works
Edge artificial intelligence for 6g: vision, enabling technologies, and applications
Letaief, K. B., Shi, Y., Lu, J., & Lu, J. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3126076 · Cited by 829 · see citing works
“SLAM comprises simultaneously estimating the state of a vehicle and constructing a map of the environment [304], which paves the way for achieving full autonomy in autonomous driving [305].”
BackgroundMultimodal end-to-end autonomous driving
Xiao, Y., Codevilla, F., Gurram, A., Urfalioglu, O., & Lopez, A. M. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3013234 · Cited by 319 · see citing works
On Offline Evaluation of Vision-Based Driving Models
Codevilla, F., López, A. M., Koltun, V., & Dosovitskiy, A. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01267-0_15 · Cited by 103 · see citing works
- Authors
- Bresson, G., Alsayed, Z., Yu, L., & Glaser, S.
- Affiliations
- VeDeCoM Institute (FR)
- Institut national de recherche en sciences et technologies du numérique (FR)
- Venue
- IEEE Transactions on Intelligent Vehicles
- Published
- 2017-09
- Volume
- 2
- Issue
- 3
- Pages
- 194-220
- ISSN
- 2379-8858 (ISSN-L)
- 2379-8904
- OpenAlex
- W2750632489
- Cited by
- 885
- References
- 281
- Citations by year
- 2026: 55 · 2025: 89 · 2024: 131 · 2023: 128 · 2022: 147 · 2021: 112 · 2020: 113 · 2019: 72 · 2018: 37 · 2017: 1
- FWCI
- 56.14
- Type
- journal article
- Language
- en
- Open access
- green
- hal.science
- Locations
- IEEE Transactions on Intelligent Vehicles (publishedVersion)
- HAL (submittedVersion, other-oa)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- SLAM, autonomous driving, autonomous vehicles, distributed SLAM, weather robustness
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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