HTC Vive as a Ground-Truth System for Anchor-Based Indoor Localization

The last decade has seen a surge in the popularity of indoor localization systems. Researchers and companies keep searching for technologies that can locate users on a large scale with low costs and the highest possible accuracy. When evaluating the accuracy of a localization system, there is a trade-off between the cost and labor involved in acquiring ground-truth measurements. The cheapest option is to acquire measurements in fixed spots and manually compute their true location in a local coordinate system using distance measuring tools. However, this method is prone to human errors and has a high setup overhead. In contrast, high-end motion capture systems are easy to set up but have prohibitive prices. A middle-of-the-road solution is to use a consumer-grade motion capture system such as the HTC Vive which, although designed for virtual reality video games, can be adapted for scientific applications. We propose a ground-truth system for anchor-based indoor localization systems which builds on the HTC Vive and we demonstrate its use on ultra-wideband (UWB) localization. We apply Procrustes Analysis to bring location data sets into the coordinate system of a room, in order to easily overlap, visualize, and analyze measurements. We use the HTC Vive to acquire the locations of UWB anchors, which allows users to quickly test which hardware placement yields the lowest localization error. The resulting ground-truth system costs under $1000, has an average accuracy of more than 5 mm, is easy to set up, and can be used for both static and dynamic measurements.

[1]  Erik Wolfart,et al.  Localization and tracking in known large environments using portable real-time 3D sensors , 2016, Comput. Vis. Image Underst..

[2]  Swarun Kumar,et al.  Decimeter-Level Localization with a Single WiFi Access Point , 2016, NSDI.

[3]  Fredrik Gustafsson,et al.  Positioning using time-difference of arrival measurements , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[4]  Kristofer S. J. Pister,et al.  RF Time of Flight Ranging for Wireless Sensor Network Localization , 2006, 2006 International Workshop on Intelligent Solutions in Embedded Systems.

[5]  Andreas F. Molisch,et al.  HiPR: High-Precision UWB Ranging for Sensor Networks , 2019, MSWiM.

[6]  Niilo Sirola Closed-form algorithms in mobile positioning: Myths and misconceptions , 2010, 2010 7th Workshop on Positioning, Navigation and Communication.

[7]  Anton Ledergerber,et al.  Calibrating Away Inaccuracies in Ultra Wideband Range Measurements: A Maximum Likelihood Approach , 2018, IEEE Access.

[8]  Kay Römer,et al.  Demo Abstract: SnapLoc: An Ultra-Fast UWB-Based Indoor Localization System for an Unlimited Number of Tags , 2019, 2019 18th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).

[9]  Jie Liu,et al.  The Microsoft Indoor Localization Competition: Experiences and Lessons Learned , 2015, IEEE Signal Processing Magazine.

[10]  P. Schönemann,et al.  A generalized solution of the orthogonal procrustes problem , 1966 .

[11]  Gonçalo Lopes,et al.  HiveTracker: 3D positioning for ubiquitous embedded systems , 2019, UbiComp/ISWC Adjunct.

[12]  Kay Römer,et al.  SnapLoc: An Ultra-Fast UWB-Based Indoor Localization System for an Unlimited Number of Tags , 2019, 2019 18th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).

[13]  Paul Patras,et al.  Dead on Arrival: An Empirical Study of The Bluetooth 5.1 Positioning System , 2019, WiNTECH.

[14]  Hong Linh Truong,et al.  MQTT-S — A publish/subscribe protocol for Wireless Sensor Networks , 2008, 2008 3rd International Conference on Communication Systems Software and Middleware and Workshops (COMSWARE '08).

[15]  Brian Coltin,et al.  HTC Vive: Analysis and Accuracy Improvement , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[16]  Peter Stone,et al.  A Low Cost Ground Truth Detection System for RoboCup Using the Kinect , 2011, RoboCup.

[17]  Dana H. Brooks,et al.  Closed-form solution for positioning based on angle of arrival measurements , 2002, The 13th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications.

[18]  Mounir Ghogho,et al.  Effects of anchor placement on mean-CRB for localization , 2011, 2011 The 10th IFIP Annual Mediterranean Ad Hoc Networking Workshop.

[19]  Sachin Katti,et al.  SpotFi: Decimeter Level Localization Using WiFi , 2015, SIGCOMM.

[20]  Yunhao Liu,et al.  From RSSI to CSI , 2013, ACM Comput. Surv..

[21]  Kin K. Leung,et al.  A Survey of Indoor Localization Systems and Technologies , 2017, IEEE Communications Surveys & Tutorials.

[22]  Li Li,et al.  The Accuracy and Precision of Position and Orientation Tracking in the HTC Vive Virtual Reality System for Scientific Research , 2017, i-Perception.