Comprehensive Airborne Laser Scanning (ALS) Simulation

被引:8
作者
Dayal, Shikhar [1 ]
Goel, Salil [1 ]
Lohani, Bharat [1 ]
Mittal, Namit [1 ]
Mishra, R. K. [2 ]
机构
[1] Indian Inst Technol, Dept Civil Engn, Kanpur, Uttar Pradesh, India
[2] Univ New, Dept Geodesy & Geomat Engn, Brunswick, ME USA
关键词
ALS; LiDAR simulator; Camera simulator; Data simulation; Limulator; LIDAR DATA; SYSTEM; GENERATION;
D O I
10.1007/s12524-021-01334-5
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The 3D topographic data generated by airborne laser scanning (ALS) has numerous applications such as urban planning, transportation planning, flood modeling and mapping. Various types of LiDAR (light detection and ranging) datasets are needed for testing and development of algorithms for these applications. Such datasets are not readily available as the LiDAR data capturing process is complex, time-consuming and expensive. Simulated ALS data can be a low-cost alternative to the expensive LiDAR datasets. This paper presents the development of a simulator (called Limulator 4.0) for ALS data. Limulator includes four components, namely 3D model component, camera component, laser component and LiDAR data generation. The first component provides a user, the facility to input user-defined 3D models of various objects. The laser component mathematically models the scanner movement and flight trajectory to generate simulated ALS parameters. The camera component takes user-defined parameters to generate images captured by a virtual camera. The effects of external and internal forces on flight movement and various errors are also modeled mathematically. Software for this simulation is developed in C + + , and its user-friendly GUI is developed using QT Creator 5.6. Limulator can serve as the ideal testbed for developing, testing and validation of various algorithms.
引用
收藏
页码:1603 / 1622
页数:20
相关论文
共 32 条
[1]   Mapping urban forest leaf area index with airborne lidar using penetration metrics and allometry [J].
Alonzo, Michael ;
Bookhagen, Bodo ;
McFadden, Joseph P. ;
Sun, Alex ;
Roberts, Dar A. .
REMOTE SENSING OF ENVIRONMENT, 2015, 162 :141-153
[2]  
[Anonymous], 1994, NASA TECHNICAL MEMOR
[3]  
Beinat, 2002, INT ARCH PHOTOGRAMME, V34, P36
[4]   A NOTE ON THE GENERATION OF RANDOM NORMAL DEVIATES [J].
BOX, GEP ;
MULLER, ME .
ANNALS OF MATHEMATICAL STATISTICS, 1958, 29 (02) :610-611
[5]  
Cho, 2019, 2019 16 INT C UB ROB, DOI 10.1109/URAI.2019.8768571
[6]  
Endo T., 2012, 22 ISPRS C AUG 25 SE
[7]  
Garcia-Gutierrez J., 2016, LECT NOTES COMPUTER, V9648
[8]   Forest biomass estimation from airborne LiDAR data using machine learning approaches [J].
Gleason, Colin J. ;
Im, Jungho .
REMOTE SENSING OF ENVIRONMENT, 2012, 125 :80-91
[9]   Relative Contribution and Effect of Various Error Sources on the Performance of Mobile Mapping System (MMS) [J].
Goel, Salil ;
Lohani, Bharat .
JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING, 2015, 43 (03) :639-645
[10]   Deep learning-based tree classification using mobile LiDAR data [J].
Guan, Haiyan ;
Yu, Yongtao ;
Ji, Zheng ;
Li, Jonathan ;
Zhang, Qi .
REMOTE SENSING LETTERS, 2015, 6 (11) :864-873