ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-4-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2021, 121–127, 2021
https://doi.org/10.5194/isprs-annals-V-4-2021-121-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2021, 121–127, 2021
https://doi.org/10.5194/isprs-annals-V-4-2021-121-2021

  17 Jun 2021

17 Jun 2021

AUGMENTED REALITY ASSET TRACKING USING HOLOLENS

J. I. Fan and K. Khoshelham J. I. Fan and K. Khoshelham
  • Dept. of Infrastructure Engineering, The University of Melbourne, Parkville 3010, Australia

Keywords: Asset Tracking, Asset Mapping, Asset Detection, Augmented Reality, Indoor Mapping, Smart Glasses, Photogrammetry

Abstract. Asset Tracking is an essential component of building management process. It involves creating and maintaining a database of detailed information of assets such as location, condition, brand, and type. This information can help building owners make informed decisions for cost-effective maintenance of building assets. Existing approaches to perform asset tracking require a manual process of measuring and recording the asset condition and location, which is labour-intensive and costly. The typical approach usually includes a human operator with pen and paper inspecting the site and manually recording the information about the asset. In this paper, we propose an augmented reality asset tracking system using HoloLens to reduce the manual labour involved in this process. The system can automatically detect the asset, record and update its related information by visual inspection. Assets are detected by feeding images captured by the HoloLens built-in camera to a pre-trained object detection network. Using a combination of various sensor readings from the HoloLens, the system can estimate the location of the asset using visual simultaneous localization and mapping (vSLAM). This information is then viewed and verified by the user using the augmented reality user interface. Upon the user confirmation, this information will be uploaded to a database. As a case study, we demonstrate a vending machine tracking system which is able to detect and localise the vending machines in an indoor environment and create a database of vending machine information. The system can detect vending machines with a mean average precision of 94.8% and a localization accuracy of 2.3 meters without pre-screening or user input.