GeoIoT World Conference的室內地理定位的測試平台實證實驗的報告:室外用解決方案10項產品的多元的評估
GeoIoT World Indoor Location Testbed Report: A Multi-Dimensional Evaluation of Ten Outdoor Solutions
|出版商||Grizzly Analytics LLC||商品編碼||370307|
|出版日期||內容資訊||英文 51 Pages
|GeoIoT World Conference的室內地理定位的測試平台實證實驗的報告:室外用解決方案10項產品的多元的評估 GeoIoT World Indoor Location Testbed Report: A Multi-Dimensional Evaluation of Ten Outdoor Solutions|
|出版日期: 2016年06月23日||內容資訊: 英文 51 Pages||
本報告依據2016年5月舉辦的「GeoIoT World Conference」的發表內容，提供室內地理定位在測試平台的實證實驗的內容、成果、趨勢等相關分析，此次使用的測試平台的設定，及檢驗對象的解決方案 (共10項產品)的概要、特性，檢驗結果的資訊彙整，為您概述為以下內容。
This report presents the results and analysis of the Indoor Location Testbed at the GeoIoT World Conference, in May, 2016. The Indoor Location Testbed was the first industrial evaluation of indoor location solutions that measured a wide range of real-world metrics and produced a multi-dimensional analysis that reflects the complexity of the area.
Ten solutions were evaluated from eight companies: BlooLoc (phone and tag solutions), GipsTech, Here (Bluetooth and Wi-Fi solutions), Indoo.rs, Lambda4, Movin, NexToMe and Senion. The solutions cross the spectrum of indoor location solutions. Most run on smartphones, but some use dedicated hardware. Many use beacons, but some are infrastructure-free or use signals of opportunity.
Two solutions were the top performers among the phone-based solutions, combining the strongest accuracy with stabilization, consistency and setup time. One solution was strongest in the infrastructure-free segment, with performance competitive with beacon based solutions. One solution was the strongest in the hardware based segment. All the testbed participants performed very well compared to industry norms.
The testbed analysis confirms the great advances that the industry has made in recent years. Infrastructure-free solutions and Wi-Fi based solutions achieved high accuracy as well as many of the beacon-based solutions. SLAM and SLAM-like technology succeeded in reducing the setup time required for some solutions. Solutions using dedicated hardware achieved even better results than the smartphone based solutions. Solutions using motion sensing succeeded in avoiding the accumulation of error over time. Several solutions achieved accuracy levels better than 2 meters when measuring after stabilization.
This analysis demonstrates that indoor location solutions are finally meeting market needs. Accuracy is improving and getting more consistent, setup time is going down, infrastructure requirements can be eliminated if desired, and SLAM technologies are enabling systems to be self-learning and adaptive. The upcoming year promises to be a very exciting one for indoor location technologies.