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High-Definition Map Generation Technologies For Autonomous Driving

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arxiv 2206.05400 v2 pith:6V57VQG5 submitted 2022-06-11 cs.RO cs.CV

classification cs.ROcs.CV
keywords autonomousdrivinggenerationmapsresearcherstechnologieshigh-definitionobject
verification ladder T0 review T1 audit T2 compute T3 formal
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Autonomous driving has been among the most popular and challenging topics in the past few years. On the road to achieving full autonomy, researchers have utilized various sensors, such as LiDAR, camera, Inertial Measurement Unit (IMU), and GPS, and developed intelligent algorithms for autonomous driving applications such as object detection, object segmentation, obstacle avoidance, and path planning. High-definition (HD) maps have drawn lots of attention in recent years. Because of the high precision and informative level of HD maps in localization, it has immediately become one of the critical components of autonomous driving. From big organizations like Baidu Apollo, NVIDIA, and TomTom to individual researchers, researchers have created HD maps for different scenes and purposes for autonomous driving. It is necessary to review the state-of-the-art methods for HD map generation. This paper reviews recent HD map generation technologies that leverage both 2D and 3D map generation. This review introduces the concept of HD maps and their usefulness in autonomous driving and gives a detailed overview of HD map generation techniques. We will also discuss the limitations of the current HD map generation technologies to motivate future research.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. FlexCloud: Direct, Modular Georeferencing and Drift-Correction of Point Cloud Maps

    cs.RO 2025-02 conditional novelty 6.0 of 10

    FlexCloud georeferences and drift-corrects SLAM point cloud maps using a GNSS-based 3D rubber-sheet transformation with automatically selected control points.

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