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OPTIMAL POSITION AND PATH PLANNING FOR STOP-AND-GO LASERSCANNING FOR THE ACQUISITION OF 3D BUILDING MODELS.
- Source :
- ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences; 2022, Issue 4, p129-136, 8p
- Publication Year :
- 2022
-
Abstract
- Terrestrial laser scanning has become more and more popular in recent years. The according planning of the standpoint network is a crucial issue influencing the overhead and the resulting point cloud. Fully static approaches are both cost and time extensive, whereas fully kinematic approaches cannot produce the same data quality. Stop-and-go scanning, which combines the strengths of both strategies, represents a good alternative solution. In the scanning process, the standpoint planning is by now mostly a manual process based on expert knowledge and relying on the surveyor's experience. This paper provides a method based on Mixed Integer Linear Programming (MILP) ensuring an optimal placement of scanner standpoints considering all scanner-related constraints (e.g. incidence angle), a full coverage of the scenery, a sufficient overlap for the subsequent registration and an optimal route planning solving a Traveling Salesperson Problem (TSP). This enables the fully automatic application of autonomous systems for providing a complete model while performing a stop-and-go laser scanning, e.g. with the Spot robot from Boston Dynamics. Our pre-computed solution, i.e. standpoints and trajectory, has been evaluated surveying a real-world environment using a 360° panoramic laser scanner and successfully compared with a precise LoD2 building model of the underlying scene. The performed ICP-based registration issued from our fully automatic pipeline turns out to be a very good and safe alternative of the otherwise laborious target-based registration. [ABSTRACT FROM AUTHOR]
- Subjects :
- MIXED integer linear programming
OPTICAL scanners
POINT cloud
Subjects
Details
- Language :
- English
- ISSN :
- 21949042
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences
- Publication Type :
- Academic Journal
- Accession number :
- 158183946
- Full Text :
- https://doi.org/10.5194/isprs-annals-V-4-2022-129-2022