Calibration and Georeferencing for GNSS-Equipped Vehicle Video Mapping Using a Tesla Model Y

16 Sept 2026
Application Dome
Mobile Data Collection & Analysis
Professional mobile mapping systems achieve centimeter-level accuracy through the tight integration of navigation-grade IMUs, survey-grade GNSS, and calibrated laser scanners. This paper assesses the relative and absolute accuracy of a vehicle-mounted test platform that combines a NovAtel SPAN tightly coupled GNSS/IMU with a mid-grade Velodyne VLP-16 LiDAR, evaluated along a 1.2 km test loop on The Ohio State University campus. The mobile-cloud georeferencing is driven entirely by the SPAN-processed GNSS/IMU trajectory, post-processed in NovAtel Inertial Explorer; three additional survey-grade PPK GNSS receivers are processed independently and serve as cross-checks on the trajectory. Direct georeferencing is performed in the standard ECEF formulation with per-point trajectory interpolation. An independent reference point cloud of the same area is acquired with a Leica RTC360 terrestrial laser scanner, registered and tied down to GNSS-derived ground control so that the TLS cloud carries an independent absolute geodetic datum. Absolute accuracy is then evaluated directly: identifiable features on building façades are coordinated independently in each cloud, and the coordinate differences between the mobile-cloud and TLS positions of the same features quantify the absolute georeferencing error of the directly georeferenced mobile cloud. Relative accuracy is evaluated separately from the internal geometry of each cloud: structure dimensions, inter-feature distances, and inter-feature angles are measured in the mobile cloud and in the TLS and compared, isolating the shape-preservation performance of the platform from any constant offset between the two reference frames. An auxiliary SHARE SLAM S20 handheld scanner mounted on the same vehicle is described as supplementary platform context; its data is not used in the accuracy assessment due to unreliable GNSS/IMU/SLAM integration at vehicle speeds.
Session Moderator
Juraj Holub
Juraj Holub
Speakers
Rami Tamimi
Prof. Rami Tamimi, Professional Surveyor, Professor - The Survey School

Tags

Datenmanagement / Datenintegration // Data Management / Data Integration,GNSS,Laserscanning und LiDAR // Laser Scanning and LiDAR,Mobile Mapping,Photogrammetrie / Fernerkundung // Photogrammetry and Remote Sensing,Reality Capturing // Extended Reality,Sensoren // Sensors,Ingenieurgeodäsie // Engineering Geodesy,LiDAR

Language

Englisch // English