LIDAR data processing for scalable compression

2013 ◽  
Author(s):  
Ruben D. Nieves
Keyword(s):  
Data Processing ◽  
Lidar Data

Regularized algorithm for Raman lidar data processing

2007 ◽  
Vol 46 (22) ◽  
pp. 4879 ◽  
Author(s):  
Valery Shcherbakov
Keyword(s):  
Data Processing ◽  
Lidar Data ◽  
Raman Lidar

TITAN to Google Earth: Workflow for Data Processing for Mobile Terrestrial LIDAR

2017 ◽  
Author(s):  
Michael Martin
Keyword(s):  
Data Processing ◽  
Spatial Information ◽  
New Technology ◽  
Google Earth ◽  
Lidar Data ◽  
Terrestrial Lidar ◽  
Urban Modelling ◽  
Feature Based ◽  
Point Data ◽  
Object Models

Terrestrial LIDAR scanners are pushing the boundaries of accurate urban modelling. Automation and the usability of tools used in feature abstraction and, to a lesser degree, presentation have become the chief concerns with this new technology. To broaden the use and impact of LIDAR in the geomatics, LiDAR datasets must be converted to feature-based representations without loss of precision. One approach, taken here, is to simultaneously examine the overall path that data takes through an organization and the operatordriven tasks carried out on the data as it is transformed from a raw point cloud to final product. We present a review of the current practices in LiDAR data processing and a foundation for future efforts to optimize. We examine alternative LIDAR processing workflows with two key questions in mind: computational efficiency - whether the process can be done using the tools at all - and tool complexity - what operator skill level is needed at each step. Using these workflows the usability of the specific software tools and the required knowledge to effectively carry out the procedures using the tools are examined. Preliminary results have yielded workflows that successfully translate LIDAR to 3D object models, highly decimated point representations of street data represented in Google Earth, and large volume point data flythroughs in ESRI ArcScene. We are documenting the pragmatic limits on each of these workflows and tools for endusers. Terrestrial LIDAR brings with it new innovations for spatial visualizations, but also questions of viability. The technology has proved valuable for specialized applications for experts, but can it be useful as a tool for proliferating 3d spatial information by and to non-experts. This study illustrates the issues associated with preparing 3d LIDAR data for presentation in mainstream visualization environments.

Basics of LiDAR Data Processing

2017 ◽  
pp. 41-62
Author(s):  
Pinliang Dong ◽  
Qi Chen
Keyword(s):  
Data Processing ◽  
Lidar Data

scholarly journals A Microtopographic Feature Analysis-Based LiDAR Data Processing Approach for the Identification of Chu Tombs

2017 ◽  
Vol 9 (9) ◽  
pp. 880 ◽  
Author(s):  
Shaohua Wang ◽  
Qingwu Hu ◽  
Fengzhu Wang ◽  
Mingyao Ai ◽  
Ruofei Zhong
Keyword(s):  
Data Processing ◽  
Feature Analysis ◽  
Lidar Data ◽  
Processing Approach

scholarly journals Comparison of data-processing algorithms for the lidar detection of mackerel in the Norwegian Sea

2009 ◽  
Vol 66 (6) ◽  
pp. 1023-1028 ◽  
Author(s):  
James H. Churnside ◽  
Eirik Tenningen ◽  
James J. Wilson
Keyword(s):  
Data Processing ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Norwegian Sea ◽  
Fish Schools ◽  
Processing Algorithms ◽  
High Level ◽  
Processing Techniques ◽  
Data Processing Algorithms ◽  
Broad Scale

Abstract Churnside, J. H., Tenningen, E., and Wilson, J. J. 2009. Comparison of data-processing algorithms for the lidar detection of mackerel in the Norwegian Sea. – ICES Journal of Marine Science, 66: 1023–1028. A broad-scale lidar survey was conducted in the Norwegian Sea in summer 2002. Since then, various data-processing techniques have been developed, including manual identification of fish schools, multiscale median filtering, and curve fitting of the lidar profiles. In the automated techniques, applying a threshold to the data, as carrried out already to eliminate plankton scattering, has been demonstrated previously to improve the correlation between lidar and acoustic data. We applied these techniques to the lidar data of the 2002 survey and compared the results with those of a mackerel (Scomber scombrus) survey done by FV “Endre Dyrøy” and FV “Trønderbas” during the same period. Despite a high level of variability in both lidar and trawl data, the broad-scale distribution of fish inferred from the lidar agreed with that of mackerel caught by the FV “Endre Dyrøy”. This agreement was obtained using both manual and automated processing of the lidar data. This work is the first comparison of concurrent lidar and trawl surveys, and it demonstrates the utility of airborne lidar for mackerel studies.

scholarly journals Distributed LiDAR data processing in a high-memory cloud-computing environment

Annals of GIS ◽  
2014 ◽  
Vol 20 (4) ◽  
pp. 255-264 ◽  
Author(s):  
James W. Hegeman ◽  
Vivek B. Sardeshmukh ◽  
Ramanathan Sugumaran ◽  
Marc P. Armstrong
Keyword(s):  
Cloud Computing ◽  
Data Processing ◽  
Lidar Data ◽  
Computing Environment ◽  
Cloud Computing Environment

Obstacle Detection for Autonomous Driving Vehicles With Multi-LiDAR Sensor Fusion

2019 ◽  
Vol 142 (2) ◽  
Author(s):  
Mingcong Cao ◽  
Junmin Wang
Keyword(s):  
Data Processing ◽  
Autonomous Vehicles ◽  
Hybrid Electric Vehicle ◽  
Spatial Clustering ◽  
Autonomous Driving ◽  
Obstacle Detection ◽  
Lidar Data ◽  
Detection Accuracy ◽  
Clustering And Classification ◽  
Systematic Solution

Abstract In contrast to the single-light detection and ranging (LiDAR) system, multi-LiDAR sensors may improve the environmental perception for autonomous vehicles. However, an elaborated guideline of multi-LiDAR data processing is absent in the existing literature. This paper presents a systematic solution for multi-LiDAR data processing, which orderly includes calibration, filtering, clustering, and classification. As the accuracy of obstacle detection is fundamentally determined by noise filtering and object clustering, this paper proposes a novel filtering algorithm and an improved clustering method within the multi-LiDAR framework. To be specific, the applied filtering approach is based on occupancy rates (ORs) of sampling points. Besides, ORs are derived from the sparse “feature seeds” in each searching space. For clustering, the density-based spatial clustering of applications with noise (DBSCAN) is improved with an adaptive searching (AS) algorithm for higher detection accuracy. Besides, more robust and accurate obstacle detection can be achieved by combining AS-DBSCAN with the proposed OR-based filtering. An indoor perception test and an on-road test were conducted on a fully instrumented autonomous hybrid electric vehicle. Experimental results have verified the effectiveness of the proposed algorithms, which facilitate a reliable and applicable solution for obstacle detection.

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