Abstract
Mobile Laser Scanning (MLS) technology acquires a huge volume of data in a very short time. In many cases, it is reasonable to reduce the size of the dataset with eliminating points in such a way that the datasets, after reduction, meet specific optimization criteria. Various methods exist to decrease the size of point cloud, such as raw data reduction, Digital Terrain Model (DTM) generalization or generation of regular grid. These methods have been successfully applied on data captured from Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS), however, they have not been fully analyzed on data captured by an MLS system. The paper presents our new approach, called the Optimum Single MLS Dataset method (OptD-single-MLS), which is an algorithm for MLS data reduction. The tests were carried out in two variants: (1) for raw sensory measurements and (2) for a georeferenced 3D point cloud. We found that the OptD-single-MLS method provides a good solution in both variants; therefore, the choice of the reduction variant depends only on the user.
Highlights
Mobile Laser Scanning (MLS) systems typically consist of a laser scanner, a Global Positioning System (GPS) receiver, an Inertial Measurement Unit (IMU), and cameras installed on a platform
The data collected with MLS is processed with algorithms embedded in a special software which is dedicated only to MLS data; in the case of the 3D point cloud, it has been developed for processing Terrestrial Laser Scanning (TLS) or Airborne Laser Scanning (ALS) data
This paper is the continuation of our ongoing effort to develop an efficient data reduction and presents the modification of the Optimum Dataset method, with one criterion for MLS data captured by Velodyne sensors
Summary
Mobile Laser Scanning (MLS) systems typically consist of a laser scanner, a Global Positioning System (GPS) receiver, an Inertial Measurement Unit (IMU), and cameras installed on a platform. A review of conventional MLS systems and their accuracy assessments can be found in studies by Hruza et al, Mikrut et al, and Barber et al [6,7,8] They used RTK-GPS (Real Time Kinematic) measurements to collect reference data on two test sites to validate the geometric accuracy of the Streetmapper MLS system. A mapping project often has a particular interest, and this interest can be achieved using a reduced data, for instance, an assessment of the road condition can be achieved by creating cross-sections of the road [12] and data preparation for classification [13,14] Such an approach saves time and affects the speed of work. This paper is the continuation of our ongoing effort to develop an efficient data reduction and presents the modification of the Optimum Dataset method, with one criterion for MLS data captured by Velodyne sensors (called OptD-single-MLS). The results of the processing were subjected to statistical-emiric analyzes
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