1. Advances in Mobile Mapping Technologies.
- Author
-
Lehtola, Ville, Goulette, François, Lehtola, Ville, and Nüchter, Andreas
- Subjects
History of engineering & technology ,Technology: general issues ,3D camera ,3D city model ,3D mapping ,3D simulation ,3D surveying ,6-DoF ,CAM localization ,CNN features ,DSIEKF ,DTM ,F-CNN ,Helmert variance component estimation ,IEKF ,LiDAR ,Lidar ,MSS ,Mobile Laser Scanning ,Ouster OS-1-64 ,Pandar64 ,RGB-D ,RetinaNet ,TLS reference point clouds ,V-SLAM ,accuracy ,boresight angles ,building 3D modelling ,configuration analysis ,control points ,controllability ,convolutional neural networks ,correlation coefficient ,data fusion ,dataset ,deep learning ,embedded-systems ,evaluation ,exposure control ,geometrical constraints ,georeferencing ,graph matching ,guidance ,imaging network design ,inception ,laser scanning ,lever arm ,line feature matching method ,loop closure detection ,manhole cover ,mobile laser scanning ,mobile mapping ,mobile mapping systems ,outdoor ,parking statistics ,path planning ,performance evaluation ,photogrammetry ,plane-based calibration field ,point and line features ,point cloud ,point cloud coverage ,point cloud density ,point clouds ,real-time ,robot operating system ,scene completion ,semantic ,semantic topology graph ,smart city ,synthetic ,transfer learning ,vehicle detection ,view planning ,visual SLAM ,visual-inertial odometry - Abstract
Summary: Mobile mapping is applied widely in society, for example, in asset management, fleet management, construction planning, road safety, and maintenance optimization. Yet, further advances in these technologies are called for. Advances can be radical, such as changes to the prevailing paradigms in mobile mapping, or incremental, such as the state-of-the-art mobile mapping methods. With current multi-sensor systems in mobile mapping, laser-scanned data are often registered in point clouds with the aid of global navigation satellite system (GNSS) positioning or simultaneous localization and mapping (SLAM) techniques and then labeled and colored with the aid of machine learning methods and digital camera data. These multi-sensor platforms are beginning to undergo further advancements via the addition of multi-spectral and other sensors and via the development of machine learning techniques used in processing this multi-modal data. Embedded systems and minimalistic system designs are also attracting attention, from both academic and commercial perspectives.This book contains the accepted publications of the Special Issue 'Advances in Mobile Mapping Technologies' of the Remote Sensing journal. It consists of works introducing a new mobile mapping dataset ('Paris CARLA 3D'), system calibration studies, SLAM topics, and multiple deep learning works for asset detection. We, the Guest Editors, Ville Lehtola from University of Twente, Netherlands, Andreas Nüchter from University of Würzburg, Germany, and François Goulette from Mines Paris- PSL University, France, wish to thank all the authors who contributed to this collection.