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Modern transportation infrastructure relies heavily on accurate data acquisition for effective management and maintenance. This paper explores using Mobile Light Detection and Ranging (LiDAR) systems, specifically the network survey vehicle equipped with LiDAR technology, to gather comprehensive data on road assets. The research area encompasses the Wardha-Butibori Package, where the Z + F 9012 laser scanner captured high-resolution 3D point cloud data of pavement conditions and inventory details. Additionally, Ladybug 5 + cameras provided corridor imagery, while the POS LV system ensured precise positioning solutions. Data processing involved raw data collection, software-based exportation, and mapping using Orbit 3DM. The results showcase detailed inventories of roadside assets, including road signs, carriageway furniture, and amenities, presented in tabular form. ViaPPS desktop software generated 3D point cloud data and transversal profiles, facilitating automatic detection of road defects and markings. The study demonstrates the potential of Mobile LiDAR systems in efficiently mapping roadside assets, aiding transportation authorities in decision-making for maintenance and upgrades. This method offers a scalable solution for assessing large road networks, enabling compliance checks with design guidelines, and enhancing overall road management practices.

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Efficient Urban Mapping: Leveraging Orbit 3DM and LiDAR with Network Survey Vehicle

  • 会议论文

  • 发表日期：

    2026年

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  • Prajwal Dudhbale,
  • Sujesh Ghodmare,
  • Rohit Mane

摘要

Modern transportation infrastructure relies heavily on accurate data acquisition for effective management and maintenance. This paper explores using Mobile Light Detection and Ranging (LiDAR) systems, specifically the network survey vehicle equipped with LiDAR technology, to gather comprehensive data on road assets. The research area encompasses the Wardha-Butibori Package, where the Z + F 9012 laser scanner captured high-resolution 3D point cloud data of pavement conditions and inventory details. Additionally, Ladybug 5 + cameras provided corridor imagery, while the POS LV system ensured precise positioning solutions. Data processing involved raw data collection, software-based exportation, and mapping using Orbit 3DM. The results showcase detailed inventories of roadside assets, including road signs, carriageway furniture, and amenities, presented in tabular form. ViaPPS desktop software generated 3D point cloud data and transversal profiles, facilitating automatic detection of road defects and markings. The study demonstrates the potential of Mobile LiDAR systems in efficiently mapping roadside assets, aiding transportation authorities in decision-making for maintenance and upgrades. This method offers a scalable solution for assessing large road networks, enabling compliance checks with design guidelines, and enhancing overall road management practices.