Reduction Method for Mobile Laser Scanning Data

Błaszczak-Bąk, Wioleta; Koppanyi, Zoltan; Toth, Charles K.

doi:10.3390/ijgi7070285

Cited by 17 publications

(17 citation statements)

References 17 publications

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“…Handling such a large volume of data is time-consuming and labor-intensive. Therefore, we propose the use of the OptD method [35][36][37] to reduce the data set. Reducing the number of observations allows the 3D model and depth area to be generated much faster.…”

Section: Methodsmentioning

confidence: 99%

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Methodology for Processing of 3D Multibeam Sonar Big Data for Comparative Navigation

et al. 2019

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Autonomous navigation is an important task for unmanned vehicles operating both on the surface and underwater. A sophisticated solution for autonomous non-global navigational satellite system navigation is comparative (terrain reference) navigation. We present a method for fast processing of 3D multibeam sonar data to make depth area comparable with depth areas from bathymetric electronic navigational charts as source maps during comparative navigation. Recording the bottom of a channel, river, or lake with a 3D multibeam sonar data produces a large number of measuring points. A big dataset from 3D multibeam sonar is reduced in steps in almost real time. Usually, the whole data set from the results of a multibeam echo sounder results are processed. In this work, new methodology for processing of 3D multibeam sonar big data is proposed. This new method is based on the stepwise processing of the dataset with 3D models and isoline maps generation. For faster products generation we used the optimum dataset method which has been modified for the purposes of bathymetric data processing. The approach enables detailed examination of the bottom of bodies of water and makes it possible to capture major changes. In addition, the method can detect objects on the bottom, which should be eliminated during the construction of the 3D model. We create and combine partial 3D models based on reduced sets to inspect the bottom of water reservoirs in detail. Analyses were conducted for original and reduced datasets. For both cases, 3D models were generated in variants with and without overlays between them. Tests show, that models generated from reduced dataset are more useful, due to the fact, that there are significant elements of the measured area that become much more visible, and they can be used in comparative navigation. In fragmentary processing of the data, the aspect of present or lack of the overlay between generated models did not relevantly influence the accuracy of its height, however, the time of models generation was shorter for variants without overlay.

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Section: Methodsmentioning

confidence: 99%

“…Only those points that were significant remained. This method has been described in detail in [35][36][37].…”

Section: Optd Methodsmentioning

confidence: 99%

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Methodology for Processing of 3D Multibeam Sonar Big Data for Comparative Navigation

et al. 2019

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“…The time required for the implementation of the OptD method can be considered as negligible in the whole process of preparing the data for the DTM construction. For a file size of 682,344 KB (about 20 million points), the OptD method lasted for about 72 s (for 50% reduction) and 105 s (for 90% reduction) [37]. It allows for effective DTM generation and reducing the time and cost of LIDAR point cloud processing, which in turn enables the conduction of efficient analyses of acquired information resource.…”

Section: Optimization Of Large Datasets Based On Using Optd Single Mementioning

confidence: 99%

Down-Sampling of Point Clouds for the Technical Diagnostics of Buildings and Structures

Suchocki

Błaszczak-Bąk

2019

Geosciences

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Terrestrial laser scanning (TLS) is a non-destructive testing method for the technical assessment of existing structures. TLS has been successfully harnessed for monitoring technical surface conditions and morphological characteristics of historical buildings (e.g., the detection of cracks and cavities). TLS measurements with very high resolution should be taken to detect minor defects on the walls of buildings. High-resolution measurements are mostly needed in certain areas of interest, e.g., cracks and cavities. Therefore, reducing redundant information on flat areas without cracks and cavities is very important. In this case, automatic down-sampling of datasets according to the aforementioned criterion is required. This paper presents the use of the Optimum Dataset (OptD) method to optimize TLS dataset. A Leica ScanStation C10 time-of-flight scanner and a Z+F IMAGER 5016 phase-shift scanner were used during the research. The research was conducted on a specially prepared concrete sample and real object, i.e., a brick citadel located on the Kościuszko Mound in Cracow. The reduction of dataset by the OptD method and random method from TLS measurements were compared and discussed. The results prove that the large datasets from TLS diagnostic measurements of buildings and structures can be successfully optimized using the OptD method.

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“…Furthermore, autonomous vehicle technologies require all this information in real time, which poses an implementation problem due to current limitations of the computer capacity, processing complexity, transfer capability to the cloud [13], etc. Mobile mapping systems produce 3D high-definition information of the surrounding of the platform by integrating navigation/georeferencing and high-resolution imaging sensor data [14,15]. Autonomous vehicle technology requires real-time sensor data processing, object extraction and tracking, and then scene interpretation and finally drive control.…”

Section: Introductionmentioning

confidence: 99%

High Definition 3D Map Creation Using GNSS/IMU/LiDAR Sensor Integration to Support Autonomous Vehicle Navigation

İlçi

Toth

2020

Sensors

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Recent developments in sensor technologies such as Global Navigation Satellite Systems (GNSS), Inertial Measurement Unit (IMU), Light Detection and Ranging (LiDAR), radar, and camera have led to emerging state-of-the-art autonomous systems, such as driverless vehicles or UAS (Unmanned Airborne Systems) swarms. These technologies necessitate the use of accurate object space information about the physical environment around the platform. This information can be generally provided by the suitable selection of the sensors, including sensor types and capabilities, the number of sensors, and their spatial arrangement. Since all these sensor technologies have different error sources and characteristics, rigorous sensor modeling is needed to eliminate/mitigate errors to obtain an accurate, reliable, and robust integrated solution. Mobile mapping systems are very similar to autonomous vehicles in terms of being able to reconstruct the environment around the platforms. However, they differ a lot in operations and objectives. Mobile mapping vehicles use professional grade sensors, such as geodetic grade GNSS, tactical grade IMU, mobile LiDAR, and metric cameras, and the solution is created in post-processing. In contrast, autonomous vehicles use simple/inexpensive sensors, require real-time operations, and are primarily interested in identifying and tracking moving objects. In this study, the main objective was to assess the performance potential of autonomous vehicle sensor systems to obtain high-definition maps based on only using Velodyne sensor data for creating accurate point clouds. In other words, no other sensor data were considered in this investigation. The results have confirmed that cm-level accuracy can be achieved.

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Reduction Method for Mobile Laser Scanning Data

Cited by 17 publications

References 17 publications

Methodology for Processing of 3D Multibeam Sonar Big Data for Comparative Navigation

Methodology for Processing of 3D Multibeam Sonar Big Data for Comparative Navigation

Down-Sampling of Point Clouds for the Technical Diagnostics of Buildings and Structures

High Definition 3D Map Creation Using GNSS/IMU/LiDAR Sensor Integration to Support Autonomous Vehicle Navigation

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