Grizzly Analytics enjoyed running the indoor location testbed at the GeoIoT World Conference in May, 2016. After too long a delay, we're happy to release videos of the ten solutions evaluated at the testbed.
The videos show our team testing and measuring the accuracy of the solutions, but it is important to understand that the final analysis in the testbed report was not based on the physical measurements taken at the time, but rather based on videos and pictures taken in real-time. We did not just rank the solutions in terms of accuracy, but rather measured a variety of metrics, including real-time accuracy, accuracy after stabilization, update speed, consistency, setup and configuration time and more.
So here goes....
First up (in alphabetical order) is BlooLoc. BlooLoc had 2 solutions in the testbed, one phone-based and one that positions their own tags. BlooLoc's phone-based solution, shown in this video, combines BLE multilateration with signal propagation analysis.
BlooLoc's tag-based solution, shown in this video, achieved even better performance using their proprietary tags.
Next up is GipsTech, whose solution, shown in this video, was the only one that was fully infrastructure-free, not using any radio signals at all.
Next is Here, who also had two solutions. Their Bluetooth-based solution, shown here, used BLE beacons along with very strong signal post-processing.
Here's Wi-Fi-based solution, shown here, does the same, but with Wi-Fi signals.
Next is indoo.rs, whose solution, shown here, uses BLE beacons, with their own SLAM technology for self-configuration and automatic adaptation to changes in the environment.
Lambda4's solution, shown here, was another solution using their own hardware:
Movin's solution, shown here, combines Bluetooth fingerprinting with motion sensing, and runs fully on the handset.
NexToMe's solution, shown here, uses BLE multilateration along with models of the user's walking patterns.
Finally, Senion's solution, shown here, combines BLE fingerprinting with motion sensing.
It was lots of fun working with these companies and getting hands-on with so many great indoor location solutions.
For details and complete analysis of each solution's performance, see the testbed report.
Next up is GipsTech, whose solution, shown in this video, was the only one that was fully infrastructure-free, not using any radio signals at all.
Next is Here, who also had two solutions. Their Bluetooth-based solution, shown here, used BLE beacons along with very strong signal post-processing.
Here's Wi-Fi-based solution, shown here, does the same, but with Wi-Fi signals.
Next is indoo.rs, whose solution, shown here, uses BLE beacons, with their own SLAM technology for self-configuration and automatic adaptation to changes in the environment.
Lambda4's solution, shown here, was another solution using their own hardware:
Movin's solution, shown here, combines Bluetooth fingerprinting with motion sensing, and runs fully on the handset.
NexToMe's solution, shown here, uses BLE multilateration along with models of the user's walking patterns.
Finally, Senion's solution, shown here, combines BLE fingerprinting with motion sensing.
It was lots of fun working with these companies and getting hands-on with so many great indoor location solutions.
For details and complete analysis of each solution's performance, see the testbed report.