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Multimodal sensor dataset from vehicle-mounted mobile mapping system for comprehensive urban scenes.

Shengyu Lu1, Sheng Bao1, Wenzhong Shi2

  • 1The Otto Poon C. F. Smart Cities Research Institute, Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China.

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A new vehicle-mounted mobile mapping dataset (MSD-VMMS-HK) offers high-precision data for comprehensive urban scenes. This resource supports research in autonomous driving and digital twins, addressing limitations of portable mapping systems.

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Area of Science:

  • Geomatics and Geospatial Science
  • Robotics and Autonomous Systems
  • Computer Vision and Machine Learning

Background:

  • Mobile mapping offers superior time efficiency over traditional methods, crucial for applications like high-definition maps and smart cities.
  • Existing mobile mapping datasets often rely on portable platforms, limiting research in large-scale autonomous driving applications.
  • There is a need for comprehensive, vehicle-mounted mobile mapping datasets to advance urban scene understanding and autonomous systems.

Purpose of the Study:

  • To introduce the multimodal sensor dataset from a vehicle-mounted mobile mapping system for comprehensive urban scenes (MSD-VMMS-HK).
  • To provide a rich, high-precision, large-scale dataset for diverse urban environments, including challenging scenarios.
  • To establish a benchmark for validating point cloud and image processing techniques in urban mapping.

Main Methods:

  • Utilized a vehicle-mounted mobile mapping system equipped with high-precision (millimeter-level) LiDAR, panoramic cameras, and GNSS/INS.
  • Collected data across a wide variety of representative urban scenarios in Hong Kong, including tunnels, canyons, and coastal roads.
  • Developed a multimodal dataset enabling precise spatial and visual data fusion for urban scene analysis.

Main Results:

  • The MSD-VMMS-HK dataset provides high-precision, large-scale multimodal sensor information for complex urban environments.
  • It is the first urban-level dataset offering high-precision references for validating point cloud and image processing algorithms.
  • Demonstrated dataset applications in accurate urban canyon mapping, infrastructure management, and change detection.

Conclusions:

  • The MSD-VMMS-HK dataset significantly advances research in large-scale mobile mapping and autonomous driving.
  • This comprehensive dataset serves as a valuable resource for the academic community in developing and validating urban mapping technologies.
  • Facilitates the creation of digital twins, smart city infrastructure, and improved autonomous navigation systems.