Reprint
Mobile Mapping Technologies
Edited by
December 2019
334 pages
- ISBN978-3-03928-018-6 (Paperback)
- ISBN978-3-03928-019-3 (PDF)
This book is a reprint of the Special Issue Mobile Mapping Technologies that was published in
Engineering
Environmental & Earth Sciences
Summary
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.
Format
- Paperback
License
© 2020 by the authors; CC BY license
Keywords
cultural heritage; restoration; indoor mapping; laser scanning; wearable mobile laser system; 3D digitalization; SLAM; visual landmark sequence; indoor topological localization; convolutional neural network (CNN); second order hidden Markov model; ORB-SLAM2; binary vocabulary; small-scale vocabulary; rapid relocation; terrestrial laser scanning; tunnel central axis; tunnel cross section; enhanced RANSAC; quadric fitting; constrained nonlinear least-squares problem; visual simultaneous localization and mapping; dynamic environment; RGB-D camera; encoder; OctoMap; IMMS; indoor mapping; MLS; mobile laser scanning; SLAM; point clouds; 2D laser scanner; 2D laser range-finder; LiDAR; LRF; sensors configurations; Lidar localization system; unmanned vehicle; segmentation-based feature extraction; category matching; multi-group-step L-M optimization; map management; indoor mapping; room type tagging; semantic enrichment; grammar; Bayesian inference; indoor localization; crowdsourcing trajectory; fingerprinting; smartphone; mobile mapping; laser scanning; self-calibration; 3D point clouds; geometric features; motion estimation; trajectory fusion; mobile mapping; sensor fusion; optical sensors; robust statistical analysis; portable mobile mapping system; handheld; 3D processing; point cloud; Vitis vinifera; terrestrial laser scanning; plant vigor; mobile mapping; precision agriculture; vine size; visual positioning; indoor scenes; automated database construction; image retrieval