Efficient, generalized indoor WiFi GraphSLAM

Huang, Joseph; Millman, David L.; Quigley, Morgan; Stavens, David; Thrun, Sebastian; Aggarwal, Alok

doi:10.1109/icra.2011.5979643

Cited by 196 publications

(127 citation statements)

References 28 publications

(26 reference statements)

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“…The WiFi GraphSLAM solution uses Wi-Fi RSS, pedometry (i.e., measured distance between two Wi-Fi scans), and gyroscope data [182]. GraphSLAM is a commonly used technique in robotics community for simultaneously estimating a trajectory and building a map offline.…”

Section: A Simultaneous Localization and Mappingmentioning

confidence: 99%

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

Laoudias¹,

Moreira

Kim

et al. 2018

IEEE Commun. Surv. Tutorials

353

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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Section: A Simultaneous Localization and Mappingmentioning

confidence: 99%

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

Laoudias¹,

Moreira

Kim

et al. 2018

IEEE Commun. Surv. Tutorials

353

171

View full text Add to dashboard Cite

show abstract

“…Also this problem formulation allows us to combine motion and observation information from multiple walks or even users. In the publication of Huang et al [15], the resulting pose distribution is only shown for a small building that allows for many loop closure constraints. Also, it is unclear how to crowd source the required data as the motion estimation approach is assumed to be known.…”

Section: Motion Estimationmentioning

confidence: 99%

“…Comparing their reconstructed maps to ours (see Figure 6) clearly indicates the superior mapping accuracy of our approach. Huang et al [15] presented an adaption of the Graph SLAM method which uses WLAN signal strengths as observations. Pedometer and gyroscope measurements are used to estimate the user's motion.…”

Section: Motion Estimationmentioning

confidence: 99%

A pocket guide to indoor mapping

Bissig

Wattenhofer

Welten

2013

2013 10th Workshop on Positioning, Navigation and Communication (WPNC)

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Abstract-In this paper, we present a way to obtain accurate WLAN signal strength maps in indoor environments, without dedicated hardware, and without a time consuming and complicated training process. We need two contributions towards this end. First, we present a novel dead-reckoning technique, to gather accurate user motion estimates. This motion data is combined with information about the signal strength of access points of the wireless infrastructure. Our second contribution lies in the efficient integration of this complementary information into a system that allows for easy mapping and requires nothing but a smartphone. All the required data is gathered from people walking casually around in an area of interest while carrying a smartphone in their pocket.

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“…Its simplicity, speed, and good accuracy have made it, even to this day, one of the most popular WiFi localization techniques [17,20,3]. Other popular Gaussian-based algorithms for WiFi localization involve Gaussian Processes (GP), whether they are used directly [14,9], within a Latent Variable Model framework (GP-LVM) [13], WiFi GraphSLAM [18] was introduced to alleviate weaknesses exhibited by GP-LVM such as its slow speed and the requirement of signature uniqueness. We note that, although promising, GP, GP-LVM, and WiFi GraphSLAM require a long parameter optimization step that make them unsuited to be used in our scenario where realtime operation is required and computational power limited.…”

Section: Related Workmentioning

confidence: 99%

Real-time WiFi localization of heterogeneous robot teams using an online random forest

2015

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In this paper we present a WiFi-based solution to the localization and mapping problem for teams of heterogeneous robots operating in unknown environments. By exploiting wireless signal strengths broadcast from access points, a robot with a large sensor payload creates a WiFi signal map that can then be shared and utilized for localization by sensor-deprived robots. In our approach, WiFi localization is cast as a classification problem. An online clustering algorithm processes incoming WiFi signals that are then incorporated into an online random forest. The algorithm's robustness is increased by a Monte Carlo Localization algorithm whose sensor model exploits the results of the online random forest classification. The proposed algorithm is shown to run in real-time, allowing the robots to operate in completely unknown environments, where a priori information such as a blue-print or the access points' location is unavailable. A comprehensive set of experiments not only compares our approach with other algorithms, but also validates the results across different scenarios covering both indoor and outdoor environments.

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Efficient, generalized indoor WiFi GraphSLAM

Cited by 196 publications

References 28 publications

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

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

A pocket guide to indoor mapping

Real-time WiFi localization of heterogeneous robot teams using an online random forest

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