Geospatial Positioning Using a Smartphone and the MS HoloLens

Abstract: During excavations, very often underground facilities are damaged, as shown in a statistics from the U.S. From all these damages, more than 36% were because of locating problems. This paper thus introduces a system that relies on Google Street View geospatial data to determine the precise position for excavation work. The visual surrounding is captured by a smartphone using Google’s ARCore and Visual Positioning System (VPS). Google’s VPS transforms the camera data into a local point cloud. Concurrently, the rough location of the smartphone is found using its GPS. This location is necessary to determine which section of the global point cloud (based on Street View data) has to be compared to the local point cloud. This comparison yields an accurate position and orientation of the smartphone. A QR code with the positional data encoded is displayed on the screen. It has to be scanned with the MS HoloLens 2 (HL2). By doing so, the HL2 is able to determine the smartphone’s position and orientation relative to itself. In combination with the information decoded from the QR code the HL2 can calculate its global position and orientation. With the positional data available on the HL2 the information of the underground facilities can be displayed. The plans are moved to the correct position, and rotated to match the orientation of the HL2. To evaluate this new system it was compared with a system based on real-time kinematics (RTK) in real-life situations. Leica’s FLX 100 plus in combination with refnet’s reference service was used to set up the RTK calibration system. With Leica’s Zeno Connect smartphone app the FLX 100 plus was connected to the reference service. An additional app was used to transfer the positional data via Bluetooth to the HL2. Both systems were examined regarding accuracy and precision under different conditions. The tolerance of the Swiss cadastral system (± 150 mm) was chosen as a reference value. To measure the deviation of the results from the real value, national and cantonal fixpoints were used. These are marked by bolts in the ground and their accuracy is < 10 mm. In order to find the offset of the rotation 5 m lines were drawn in the test plans along the edge of the sidewalk. The deviation was measured by aligning the plans correctly, the errors were equal to the displacement needed. The RTK method performed well in open areas, where almost all results under the reference value of ± 150 mm. In densely built-up areas the accuracy ranged from 1.26 - 8.66 m, and the precision from 4.41 - 13.1 m. The method with Google’s Geospatial API and only a smartphone achieved an accuracy of 0.21 to 0.43 m and a precision of 1.28 - 2.26 m in open areas. In densely built-up areas the accuracy was 0.28 - 0.36 m, and the precision 0.22 - 0.28 m. In these tests no impact due to the age of the Street view data was noticeable.

Keywords: Geospatial postioning, Virtual overlay, MS HoloLens

DOI: 10.54941/ahfe1005540

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