Abstract

Abstract. Indoor Mobile Mapping Systems (IMMSs) technologies are becoming increasingly popular thanks to the possibility of acquiring a massive amount of 3D data in a fast and effective way in those areas where GNSS signal is unavailable, like urban canyons, densely vegetated areas, underground sites and buildings. They offer an efficient way to produce point clouds but with noticeably lower accuracy than the traditional Terrestrial Laser Scanning (TLS). The present paper wants to analyse two different methods to improve the accuracy of the point cloud coming from an IMMS survey in a vast urban scenario. The first approach uses points collected during an RTK GNSS survey as Ground Control Points (GCPs). The second one involves TLS static scans as Ground Control Scans (GCSs). Both these procedures allow us to introduce constraints before generating the rigid final point cloud. The tested IMMS is the Backpack Heron MS Twin Color, produced by Gexcel S.r.l. The instrument was tested during the acquisition of the historical centre of Meda (MB) in the northern part of Italy. Results show that in those areas between the constraints, the maximum error of residuals on checkpoints is some decimetres. The IMMS has allowed us to quickly survey a vast area not otherwise obtainable with traditional survey techniques. The developed procedures proved to be essential for proper reconstruction of the environment.

Highlights

  • Indoor Mobile Mapping Systems (IMMS) technologies are becoming more and more popular thanks to the possibility of acquiring a massive amount of 3D data in a fast and effective way

  • From Mobile Mapping Systems, where the navigation system, including GNSS and an Inertial Measurement Unit (IMU), provides the trajectory and attitude for generating the georeferenced 3D point cloud, IMMSs rely on Simultaneous Localisation And Mapping (SLAM) algorithms

  • The accuracy of the final point clouds obtained with both the Ground Control Points (GCPs) and Ground Control Scans (GCSs) methods was checked using the RTK GNSS points as Check Points (CPs) and the point cloud deriving from a photogrammetric survey performed with DJI Spark as ground truth

Read more

Summary

Introduction

Indoor Mobile Mapping Systems (IMMS) technologies are becoming more and more popular thanks to the possibility of acquiring a massive amount of 3D data in a fast and effective way. These techniques can be successfully used in those areas where GNSS signal is unavailable, like urban canyons, densely vegetated areas, underground sites and buildings (Wang et al, 2020). From Mobile Mapping Systems, where the navigation system, including GNSS and an Inertial Measurement Unit (IMU), provides the trajectory and attitude for generating the georeferenced 3D point cloud, IMMSs rely on Simultaneous Localisation And Mapping (SLAM) algorithms. It is essential to highlight that both these procedures allow us to introduce constraints inside the SLAM algorithm before the generation of the rigid final point cloud

Methods
Results
Conclusion
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.