Abstract
Mobile laser scanning (MLS) is a faster and cost-effective alternative to static laser scanning, even though there is a slight trade-off in accuracy. This contribution describes a compact mobile laser scanning system mounted on a vehicle. The technical parameters of the used system components, i.e. a small LIDAR sensor Velodyne VLP-16 and a dual antenna GNSS/INS system Advanced Navigation Spatial Dual, are reviewed, along with the integration of these components for spatial data acquisition. Calculation principles of 3D coordinates from the real-time data of all the involved sensors are discussed. The field tests were carried out in a controlled environment of a parking lot and at different velocities. Experiments were carried out to test the ability of the GNSS/INS system to cope with difficult conditions, e.g. sudden movements due to cornering or swerving. The accuracy of the resulting MLS point cloud is evaluated with respect to high-accuracy static terrestrial laser scanning data. Problems regarding combining LIDAR, GNSS and INS sensors are outlined, as well as the initial accuracy assessments. Initial tests revealed errors related to insufficient quality of inertial data and a need for the trajectory post-processing calculations. Although this study was carried out while the system was mounted on a car, there is potential for operating the system on an unmanned aerial vehicle, all-terrain vehicle or in a backpack mode due to its relatively compact size.
Highlights
Mobile laser scanning (MLS) systems are used to gather 3D spatial data on the move
This paper describes an in-house assembled compact and relatively low-cost MLS system consisting of a Velodyne VLP-16 LIDAR and Advanced Navigation Spatial Dual GNSS/INS system, as well as related data processing and the accuracy assessment of the initial results
The final MLS point cloud was compared with terrestrial laser scanning (TLS) data
Summary
MLS systems are used to gather 3D spatial data (i.e. point cloud) on the move. The scientific research of MLS, of which there is a considerable amount, has mostly dealt with calibrating and data processing, mainly the automatic extraction of features from a point cloud. The point cloud calculation and direct georeferencing of MLS data share the same principles as Airborne Laser Scanning (ALS), see e.g. In feature (such as pavements, technical utilities, terrain relief, facades) extraction most of the same principles apply as with terrestrial laser scanning (TLS). Such examples include extraction of building features by Pu et al (2006), extraction of a tunnel liner by Yoon et al (2009) and reconstructing tree crowns by Pyysalo et al (2002). Recognizing and extracting features from MLS point clouds has been discussed by e.g. Pu et al (2011), Yang et al (2013) and Guan et al (2014)
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