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210512 ||| eng |
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|a books978-3-03928-019-3
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|a 9783039280186
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|a 9783039280193
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| 100 |
1 |
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|a Chiang, Kai-Wei
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| 245 |
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|a Mobile Mapping Technologies
|h Elektronische Ressource
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| 260 |
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|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2019
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| 300 |
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|a 1 electronic resource (334 p.)
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| 653 |
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|a LRF
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| 653 |
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|a tunnel cross section
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| 653 |
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|a 2D laser scanner
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| 653 |
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|a restoration
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| 653 |
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|a optical sensors
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| 653 |
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|a handheld
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| 653 |
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|a precision agriculture
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| 653 |
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|a IMMS
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| 653 |
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|a visual landmark sequence
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| 653 |
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|a rapid relocation
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| 653 |
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|a 3D digitalization
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| 653 |
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|a ORB-SLAM2
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| 653 |
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|a quadric fitting
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| 653 |
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|a History of engineering and technology / bicssc
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| 653 |
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|a indoor scenes
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| 653 |
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|a 2D laser range-finder
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| 653 |
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|a Lidar localization system
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| 653 |
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|a dynamic environment
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| 653 |
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|a enhanced RANSAC
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| 653 |
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|a plant vigor
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| 653 |
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|a RGB-D camera
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| 653 |
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|a indoor topological localization
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| 653 |
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|a self-calibration
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| 653 |
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|a semantic enrichment
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| 653 |
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|a motion estimation
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| 653 |
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|a visual positioning
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| 653 |
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|a image retrieval
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| 653 |
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|a small-scale vocabulary
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| 653 |
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|a tunnel central axis
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| 653 |
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|a wearable mobile laser system
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| 653 |
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|a multi-group-step L-M optimization
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| 653 |
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|a OctoMap
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| 653 |
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|a laser scanning
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| 653 |
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|a grammar
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| 653 |
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|a sensor fusion
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| 653 |
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|a crowdsourcing trajectory
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| 653 |
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|a SLAM
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| 653 |
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|a indoor localization
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| 653 |
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|a mobile mapping
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| 653 |
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|a geometric features
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| 653 |
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|a vine size
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| 653 |
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|a visual simultaneous localization and mapping
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| 653 |
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|a map management
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| 653 |
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|a constrained nonlinear least-squares problem
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| 653 |
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|a sensors configurations
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| 653 |
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|a second order hidden Markov model
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| 653 |
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|a cultural heritage
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| 653 |
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|a LiDAR
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| 653 |
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|a terrestrial laser scanning
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| 653 |
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|a robust statistical analysis
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| 653 |
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|a segmentation-based feature extraction
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| 653 |
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|a smartphone
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| 653 |
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|a encoder
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| 653 |
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|a point clouds
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| 653 |
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|a automated database construction
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| 653 |
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|a fingerprinting
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| 653 |
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|a Vitis vinifera
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| 653 |
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|a convolutional neural network (CNN)
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| 653 |
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|a MLS
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| 653 |
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|a 3D processing
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| 653 |
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|a room type tagging
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| 653 |
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|a category matching
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| 653 |
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|a point cloud
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| 653 |
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|a indoor mapping
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| 653 |
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|a portable mobile mapping system
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| 653 |
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|a trajectory fusion
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| 653 |
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|a mobile laser scanning
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| 653 |
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|a Bayesian inference
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| 653 |
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|a binary vocabulary
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| 653 |
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|a unmanned vehicle
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| 653 |
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|a 3D point clouds
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| 700 |
1 |
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|a Rodríguez-Gonzálvez, Pablo
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| 700 |
1 |
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|a Toschi, Isabella
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| 700 |
1 |
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|a Nocerino, Erica
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by-nc-nd/4.0/
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| 028 |
5 |
0 |
|a 10.3390/books978-3-03928-019-3
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| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/53632
|z DOAB: description of the publication
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| 856 |
4 |
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|u https://www.mdpi.com/books/pdfview/book/1900
|7 0
|x Verlag
|3 Volltext
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|a 900
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|a 363
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|a Mobile Mapping technologies have seen a rapid growth of research activity and interest in the last years, due to the increased demand of accurate, dense and geo-referenced 3D data. Their main characteristic is the ability of acquiring 3D information of large areas dynamically. This versatility has expanded their application fields from the civil engineering to a broader range (industry, emergency response, cultural heritage...), which is constantly widening. This increased number of needs, some of them specially challenging, is pushing the Scientific Community, as well as companies, towards the development of innovative solutions, ranging from new hardware / open source software approaches and integration with other devices, up to the adoption of artificial intelligence methods for the automatic extraction of salient features and quality assessment for performance verification The aim of the present book is to cover the most relevant topics and trends in Mobile Mapping Technology, and also to introduce the new tendencies of this new paradigm of geospatial science.
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