The Microsoft Indoor Localization Competition: Experiences and Lessons Learned

Lymberopoulos, Dimitrios; Liu, Jie

doi:10.1109/msp.2017.2713817

Cited by 168 publications

(105 citation statements)

References 42 publications

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“…In addition to the above popular indoor positioning sensors, the 2.4 GHz phase offset technique [15], modulated magnetic signals [16], 24-GHz radar [17], light detection and ranging (LiDAR) [18], and foot-mounted IMU [19] are proposed for the purpose. These systems participated in the Microsoft Indoor Localization Competition [20] in 2017, which judged that only LiDAR and UWB-based systems could achieve better than metre-level positioning accuracy; the LiDAR-based system can achieve centimetre-level positioning accuracy, and the UWB-based system can achieve decimetre-level positioning accuracy [20]. Although LiDAR-based technology is extremely accurate, the cost, size, and power consumption of LiDAR sensors prevent this technology from becoming a mainstream indoor positioning solution [20].…”

Section: Introductionmentioning

confidence: 99%

An Autonomous Ultra-Wide Band-Based Attitude and Position Determination Technique for Indoor Mobile Laser Scanning

Lau

Quan

Wan

et al. 2018

IJGI

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Mobile laser scanning (MLS) has been widely used in three-dimensional (3D) city modelling data collection, such as Google cars for Google Map/Earth. Building Information Modelling (BIM) has recently emerged and become prominent. 3D models of buildings are essential for BIM. Static laser scanning is usually used to generate 3D models for BIM, but this method is inefficient if a building is very large, or it has many turns and narrow corridors. This paper proposes using MLS for BIM 3D data collection. The positions and attitudes of the mobile laser scanner are important for the correct georeferencing of the 3D models. This paper proposes using three high-precision ultra-wide band (UWB) tags to determine the positions and attitudes of the mobile laser scanner. The accuracy of UWB-based MLS 3D models is assessed by comparing the coordinates of target points, as measured by static laser scanning and a total station survey.

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Section: Introductionmentioning

confidence: 99%

An Autonomous Ultra-Wide Band-Based Attitude and Position Determination Technique for Indoor Mobile Laser Scanning

Lau

Quan

Wan

et al. 2018

IJGI

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“…In 2013, the FCC Communications Security, Reliability and Interoperability Council (CSRIC) documented several emerging indoor location technologies [3] and reported extensive accuracy results of a number of commercial systems during localization of more than 13,400 test E911 calls across 19 buildings [4]. The great interest in performance evaluation of indoor localization systems under real-life conditions is also evident from newly released standards [5] and related competitions, including the Microsoft Indoor Localization Competition [6], [7], the EvAAL contest for evaluating Ambient and Assisted Living (AAL) systems through competitive benchmarking [8], [9], and most recently PerfLoc competition for smartphone indoor localization applications announced by the U.S.A. National Institute of Standards and Technology (NIST) [10].…”

mentioning

confidence: 99%

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

Laoudias¹,

Moreira

Kim

et al. 2018

IEEE Commun. Surv. Tutorials

369

171

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Abstract-Location information for events, assets, and individuals, mostly focusing on two dimensions so far, has triggered a multitude of applications across different verticals, such as consumer, networking, industrial, health care, public safety, and emergency response use cases. To fully exploit the potential of location awareness and enable new advanced location-based services, localization algorithms need to be combined with complementary technologies including accurate height estimation, i.e., three dimensional location, reliable user mobility classification, and efficient indoor mapping solutions. This survey provides a comprehensive review of such enabling technologies. In particular, we present cellular localization systems including recent results on 5G localization, and solutions based on Wireless Local Area Networks (WLAN), highlighting those that are capable of computing 3D location in multi-floor indoor environments. We overview range-free localization schemes, which have been traditionally explored in Wireless Sensor Networks (WSN) and are nowadays gaining attention for several envisioned Internet of Things (IoT) applications. We also present user mobility estimation techniques, particularly those applicable in cellular networks, that can improve localization and tracking accuracy. Regarding the mapping of physical space inside buildings for aiding tracking and navigation applications, we study recent advances and focus on smartphone-based indoor Simultaneous Localization and Mapping (SLAM) approaches.The survey concludes with service availability and system scalability considerations, as well as security and privacy concerns in location architectures, discusses the technology roadmap, and identifies future research directions.

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“…In the above mentioned competition, different localization approaches were compared under the same conditions [10]. The average error obtained by infrastructure-less system was within 1.5m and 5.3m.…”

Section: Related Workmentioning

confidence: 99%

“…Such systems can be split into two important categories: infrastructure-based and infrastructureless. While one might think that having additional infrastructure improves the localization error significantly, a recent localization competition, the Microsoft Indoor Localization Competition -IPSN 2014 [10], has shown that in practice, having the same conditions and time to calibrate such systems, they behave very similarly. More precisely, infrastructureless systems achieve a localization error in the same range as infrastructure-based systems.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Indoor Localization System

Bâce

Pignolet

2015

2015 8th IFIP Wireless and Mobile Networking Conference (WMNC)

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Indoor localization is an important topic for context aware applications. In particular, many applications for wireless devices can benefit from knowing the location of a user. Despite the huge effort from the research community to solve the localization problem, there is no widely accepted solution for localization in an indoor environment. In this paper we focus on constrained devices and propose an extremely lightweight indoor localization system that can be scaled to different devices, from smartphones to smart glasses and other devices.We devise a simple yet effective WiFi-based system with low computational complexity, which does not need any additional special infrastructure nor map or an internet connection. Our system relies on IEEE 802.11 Received Signal Strength Indicator (RSSI) values and a dead reckoning module to collect walking trajectories which are further clustered and compressed to build a sensor map. The key novelty of our work is a merging algorithm that can fuse multiple sensor maps. We evaluate our system in a real world scenario and we show that using the map produced by our merging algorithm we achieve room-level accuracy. Our system is also comparable to state of the art systems, despite the lightweight approach.

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The Microsoft Indoor Localization Competition: Experiences and Lessons Learned

Cited by 168 publications

References 42 publications

An Autonomous Ultra-Wide Band-Based Attitude and Position Determination Technique for Indoor Mobile Laser Scanning

An Autonomous Ultra-Wide Band-Based Attitude and Position Determination Technique for Indoor Mobile Laser Scanning

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

Lightweight Indoor Localization System

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