2019, Hasler, Oliver, Blaser, Stefan, Nebiker, Stephan

In this paper, we present the implementation of a smartphone-based indoor mobile mapping application based on an augmented reality (AR) framework and a subsequent performance evaluation in demanding indoor environments. The implementation runs on Android and iOS devices and demonstrates the great potential of smartphone-based 3D mobile mapping. The application includes several functionalities such as device tracking, coordinate, and distance measuring as well as capturing georeferenced imagery. We evaluate our prototype system by comparing measured points from the tracked device with ground control points in an indoor environment with two different campaigns. The first campaign consists of an open, one-way trajectory whereas the second campaign incorporates a loop closure. In the second campaign, the underlying AR framework successfully recognized the start location and correctly repositioned the device. Our results show that the absolute 3D accuracy of device tracking with a standard smartphone is around 1% of the travelled distance and that the local 3D accuracy reaches sub-decimetre level.

2018, Blaser, Stefan, Cavegn, Stefan, Nebiker, Stephan

The rapid progression in digitalization in the construction industry and in facility management creates an enormous demand for the efficient and accurate reality capturing of indoor spaces. Cloud-based services based on georeferenced metric 3D imagery are already extensively used for infrastructure management in outdoor environments. The goal of our research is to enable such services for indoor applications as well. For this purpose, we designed a portable mobile mapping research platform with a strong focus on acquiring accurate 3D imagery. Our system consists of a multi-head panorama camera in combination with two multi-profile LiDAR scanners and a MEMS-based industrial grade IMU for LiDAR-based online and offline SLAM. Our modular implementation based on the Robot Operating System enables rapid adaptations of the sensor configuration and the acquisition software. The developed workflow provides for completely GNSS-independent data acquisition and camera pose estimation using LiDAR-based SLAM. Furthermore, we apply a novel image-based georeferencing approach for further improving camera poses. First performance evaluations show an improvement from LiDAR-based SLAM to image-based georeferencing by an order of magnitude: from 10–13 cm to 1.3–1.8 cm in absolute 3D point accuracy and from 8–12 cm to sub-centimeter in relative 3D point accuracy.