HD Map Change Detection with Cross-Domain Deep Metric Learning

Heo, Minhyeok; Kim, Jiwon; Kim, Su-Jung

doi:10.1109/iros45743.2020.9340757

Cited by 18 publications

(15 citation statements)

References 14 publications

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“…But this method is only applicable to crowdsourcing vehicles equipped with LiDAR. Heo et al proposed a high-precision map change detection algorithm based on depth metric learning, which combines adversarial learning to reduce the domain spacing between images and high-precision maps [33]. Using pixel level local change detectors to detect change areas, but cannot recognize change categories and objects, and further expansion is needed for vertical map features.…”

Section: Change Estimation and Update Technology Based On Multi-sourc...mentioning

confidence: 99%

High precision map crowdsource update technology and SLAM technology - Application in autonomous driving

Liu

2024

J. Phys.: Conf. Ser.

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In response to the challenges faced by real-time dynamic updates of high-precision maps for autonomous driving in terms of high precision, high reliability, and high safety, this paper summarizes the difficulties and challenges currently faced by high-precision map updates. Accelerate the large-scale commercialization of autonomous driving high-precision maps. Thus, improving the safety and stability of intelligent vehicles and providing critical support for high-level autonomous driving. Firstly, the definition and connotation of autonomous driving maps were described, and the data characteristics and functional applications of autonomous driving maps were pointed out. Secondly, the development status and trends of high-precision map updates were reviewed, and the advantages and disadvantages of centralized map updates and crowdsourced map updates were summarized. Pointing out that crowdsource updates have become a new trend in the development of map updates. Once again, the basic architecture and key core modules of current crowdsource updates were summarized, and an analysis was conducted on key core technologies, summarizing the current status and trends of key technologies involved in crowdsource updates. The results indicate that the current technology still faces 7 main challenges, including map modeling, high-precision positioning, 3D reconstruction, fusion updates, data security, rapid review, and standard laws and regulations to address these challenges.

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Section: Change Estimation and Update Technology Based On Multi-sourc...mentioning

confidence: 99%

High precision map crowdsource update technology and SLAM technology - Application in autonomous driving

Liu

2024

J. Phys.: Conf. Ser.

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show abstract

“…This information is primarily used for vehicle localization and control [26]. Recently, there has been a growing interest in the rich semantic information contained in HD maps, and several studies [27,28,29] have utilized them for various other tasks beyond localization and control. Similarly, our study also utilizes road information from HD maps to improve the generalization performance of lane detection.…”

Section: Hd Mapsmentioning

confidence: 99%

The antitumor activity of a novel GCN2 inhibitor in head and neck squamous cell carcinoma cell lines

Lee

Keam

Kim

et al. 2023

Translational Oncology

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“…The network can segment the lane line instances and get each lane line's color and type attributes. Heo [34] integrated the image detection and HD map to detect the environment change. Jo et al [35] presented an algorithm to process, detect, and update the HD map when the environment changes.…”

Section: Related Workmentioning

confidence: 99%

Mobile Mapping System for Automatic Extraction of Geodetic Coordinates for Traffic Signs Based on Enhanced Point Cloud Reconstruction

2022

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Lidar sensors are commonly equipped on a mobile mapping system (MMS) to establish point clouds for HD map creation. However, the point clouds themselves do not contain object attributes. Therefore, human operators have to manually obtain objects' position to assign attributes for further HD map conversion, inevitably resulting in time-consuming processes and significant labor costs. To solve the above problems, in this paper, we present an MMS equipped with non-survey grade Lidar, commercial grade camera, and entry level GNSS/INS, which incorporates ground control points (GCPs) with a Normal Distribution Transform Simultaneously Localization and Mapping (NDT SLAM) refinement and fluctuation adjustment to secure both absolute position accuracy and relative position accuracy of the reconstructed point cloud. Meanwhile, a deep neural network for image detection is employed to obtain the bounding box of traffic signs from each image frame. By applying the translation and rotation transformation between Lidar points and camera pixels, intersection of the detected object in the image and Lidar scan points can be found. By accumulating extracted Lidar points of the traffic sign in several detection frames, we can then obtain an accurate 3D geodetic coordinate of the traffic signs. Experimental results show that point clouds can be reconstructed with an average 3D RMSE of only 8.6cm, and center geodetic coordinates of traffic signs can be further extracted in sub-meter accuracy to significantly reduce labor work in HD map creation.INDEX TERMS HD map, point cloud, GIS, autonomous driving.

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HD Map Change Detection with Cross-Domain Deep Metric Learning

Cited by 18 publications

References 14 publications

High precision map crowdsource update technology and SLAM technology - Application in autonomous driving

High precision map crowdsource update technology and SLAM technology - Application in autonomous driving

The antitumor activity of a novel GCN2 inhibitor in head and neck squamous cell carcinoma cell lines

Mobile Mapping System for Automatic Extraction of Geodetic Coordinates for Traffic Signs Based on Enhanced Point Cloud Reconstruction

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