Robust time-of-arrival self calibration and indoor localization using Wi-Fi round-trip time measurements

Batstone, Kenneth; Oskarsson, Magnus; Åström, Kalle

doi:10.1109/iccw.2016.7503759

Cited by 23 publications

(14 citation statements)

References 19 publications

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“…Faster robot movements might have lead to noticeable motion blur and therefore degraded accuracy in the feature tracking of the VINS. The trajectory in experiment 1 resulted in the highest anchor pose estimation error of 11.4 cm root-meansquare error (RMSE), which is however still below anchor localization errors of 59.2 cm and 15.2 cm mentioned in [34] and [35] respectively, which are solely based on range measurements. Table III shows how the sensor error model performed across the experiments.…”

Section: Methodsmentioning

confidence: 92%

Visual-inertial SLAM aided estimation of anchor poses and sensor error model parameters of UWB radio modules

Lutz

Schuster

Steidle

2019

2019 19th International Conference on Advanced Robotics (ICAR)

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Local positioning technologies based on ultrawideband (UWB) ranging have become broadly available and accurate enough for various robotic applications. In an infrastructure setup with static anchor radio modules one common problem is to determine their global positions within the world coordinate frame. Furthermore, issues like the complex radiofrequency wave propagation properties make it difficult to design a consistent sensor error model which generalizes well across different anchor setups and environments. Combining radio based local positioning systems with a visual-inertial navigation system (VINS) can provide very accurate pose estimates for calibration of the radio based localization modules and at the same time alleviate the inherent drift in visualinertial navigation. We propose an approach to utilize a visualinertial SLAM system using fish-eye stereo cameras and an IMU to estimate the anchor 6D poses as well as the parameters of an UWB module sensor error model on a micro-aerial-vehicle (MAV). Fiducial markers on all anchor radio modules are used as artificial landmarks within the SLAM system to get accurate anchor module pose estimates.

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Section: Methodsmentioning

confidence: 92%

Visual-inertial SLAM aided estimation of anchor poses and sensor error model parameters of UWB radio modules

Lutz

Schuster

Steidle

2019

2019 19th International Conference on Advanced Robotics (ICAR)

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“…The approach was based on non-linear regression analysis where the missing observations were treated as Missing Not at Random (MNAR). Similar ideas were proposed in [22,23]. However, the support for this technology depends on a used chipset.…”

Section: Related Workmentioning

confidence: 90%

Automatic Detection of Missing Access Points in Indoor Positioning System †

Górak

Luckner

2018

Sensors

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The paper presents a Wi-Fi-based indoor localisation system. It consists of two main parts, the localisation model and an Access Points (APs) detection module. The system uses a received signal strength (RSS) gathered by multiple mobile terminals to detect which AP should be included in the localisation model and whether the model needs to be updated (rebuilt). The rebuilding of the localisation model prevents the localisation system from a significant loss of accuracy. The proposed automatic detection of missing APs has a universal character and it can be applied to any Wi-Fi localisation model which was created using the fingerprinting method. The paper considers the localisation model based on the Random Forest algorithm. The system was tested on data collected inside a multi-floor academic building. The proposed implementation reduced the mean horizontal error by 5.5 m and the classification error for the floor’s prediction by 0.26 in case of a serious malfunction of a Wi-Fi infrastructure. Several simulations were performed, taking into account different occupancy scenarios as well as different numbers of missing APs. The simulations proved that the system correctly detects missing and present APs in the Wi-Fi infrastructure.

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“…The approach was based on nonlinear regression analysis, where the missing observations were treated as Missing Not at Random. A similar idea was proposed by Batstone et al [9,10]. However, such solutions are hard for broad implementation on commonly used mobile phones [11].…”

Section: State-of-the-artmentioning

confidence: 97%

“…Designing our solution, we assumed that the system should work on commonly used devices. Therefore, the solution cannot be based on a specialised chipset as in [8][9][10]. Our solution can work using standard Wi-Fi APs and mobile devices.…”

Section: State-of-the-artmentioning

confidence: 99%

Automatic Detection of Changes in Signal Strength Characteristics in a Wi-Fi Network for an Indoor Localisation System

Luckner

Górak

2020

Sensors

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This paper faces the issue of changing the received signal strength (RSS) from an observed access point (AP). Such a change can reduce the Quality of Service (QoS) of a Wi-Fi-based Indoor Localisation System. We have proposed a dynamic system based on an estimator of RSS using the readings from other APs. Using an optimal threshold, the algorithm recognises an AP that has changed its characteristics. Next, the system rebuilds the localisation model excluding the changed AP to keep QoS. For the tests, we simulated a change in the analysed Wi-Fi network by replacing the measured RSS by an RSS obtained from the same AP model that lies in another place inside the same multi-floor building. The algorithm was evaluated in simulations of an isolated single-floor building, a single-floor building and a multi-floor building. The mean increase of the localisation error obtained by the system varies from 0.25 to 0.61 m after the RSS changes, whereas the error increase without using the system is between 1.21 and 1.98 m. The system can be applied to any service based on a Wi-Fi network for various kinds of changes like a reconfiguration of the network, a local malfunction or ageing of the infrastructure.

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Robust time-of-arrival self calibration and indoor localization using Wi-Fi round-trip time measurements

Cited by 23 publications

References 19 publications

Visual-inertial SLAM aided estimation of anchor poses and sensor error model parameters of UWB radio modules

Visual-inertial SLAM aided estimation of anchor poses and sensor error model parameters of UWB radio modules

Automatic Detection of Missing Access Points in Indoor Positioning System †

Automatic Detection of Changes in Signal Strength Characteristics in a Wi-Fi Network for an Indoor Localisation System

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