Towards real-time time-of-arrival self-calibration using ultra-wideband anchors

Batstone, Kenneth; Oskarsson, Magnus; Åström, Kalle

doi:10.1109/ipin.2017.8115885

Cited by 14 publications

(16 citation statements)

References 22 publications

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“…Similar to the approach used by the authors in Batstone et al [13], we first evaluate our self-calibration algorithm in the IIoT room with the MOCAP system and then we move the the more complex environment, i.e., the Office-Lab. We do so in order to evaluate first the performance in optimal conditions, i.e.…”

Section: Resultsmentioning

confidence: 99%

“…Batstone et al [13] experimentally evaluates the use of the Random sampling paradigm (RANSAC) and minimal solvers applied to the self-calibration enigma. The advantage of their method is that a solution can be found for a small dataset and then merged sequentially to a previous solutions, leading to computationally lighter algorithms to increase performance.…”

Section: Self-calibration For Uwb Anchor Nodesmentioning

confidence: 99%

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UWB anchor nodes self-calibration in NLOS conditions: a machine learning and adaptive PHY error correction approach

et al. 2021

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Ultra-wideband (UWB) positioning performance is highly related to the accuracy of the coordinates of the fixed anchor nodes, which form the system infrastructure. The process of determining the position of the anchors is called calibration. In an anchor-based system, it is crucial for the fixed nodes to know their locations with the highest possible accuracy. However, in certain situations, it is almost impossible to perform the calibration manually, e.g., during emergency interventions. Moreover, calibration is always delicate and time-consuming. We designed an effortless and accurate selfcalibration algorithm that does not require any manual intervention to precisely pinpoint the position of the anchors. This paper presents an innovative algorithm that combines machine learning and exploits the time resolution capabilities of UWB with adaptive physical settings to enable the automatic calibration of the fixed anchor nodes, even in realistic NLOS (non-line-of-sight) conditions. The self-calibration algorithm combines iterative gradient descent to pinpoint the positions of the anchors and uses error detection and correction from a convolutional neural network. Moreover, the algorithm can use a different set of settings for each anchor pair. This is done to ensure the most robust and accurate communication between nodes. Extensive measurements were carried out to allow anchors to estimate distances among each others. Distances were then combined and processed by the self-calibration algorithm. Experimental evaluation in two complex and large environments with many obstacles and reflections shows that accuracy reached by the algorithm is about 2.4 cm on average and 95th percentile is 5.7 cm, in best case. The results refer to the relative positions among the anchors. Results prove that in order to precisely calibrate the anchors nodes in an UWB positioning system, high correctness can be obtained by combining the accuracy of UWB together with deep learning and adaptive PHY modulation schemes.

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Section: Resultsmentioning

confidence: 99%

Section: Self-calibration For Uwb Anchor Nodesmentioning

confidence: 99%

UWB anchor nodes self-calibration in NLOS conditions: a machine learning and adaptive PHY error correction approach

et al. 2021

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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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“…If the quantity of data is large enough, no receiver or transmitter locations are required; a set of range measurements is sufficient. Calibration can then be formulated as an optimization problem that minimizes the residual of the trilateration equations [ 7 , 8 , 26 , 27 , 29 ]. Results from these approaches are not always unique, for example, in the case of rotational symmetry.…”

Section: Related Workmentioning

confidence: 99%

Calibration of Visible Light Positioning Systems with a Mobile Robot

Amsters

Demeester

Stevens

et al. 2021

Sensors

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Most indoor positioning systems require calibration before use. Fingerprinting requires the construction of a signal strength map, while ranging systems need the coordinates of the beacons. Calibration approaches exist for positioning systems that use Wi-Fi, radio frequency identification or ultrawideband. However, few examples are available for the calibration of visible light positioning systems. Most works focused on obtaining the channel model parameters or performed a calibration based on known receiver locations. In this paper, we describe an improved procedure that uses a mobile robot for data collection and is able to obtain a map of the environment with the beacon locations and their identities. Compared to previous work, the error is almost halved. Additionally, this approach does not require prior knowledge of the number of light sources or the receiver location. We demonstrate that the system performs well under a wide range of lighting conditions and investigate the influence of parameters such as the robot trajectory, camera resolution and field of view. Finally, we also close the loop between calibration and positioning and show that our approach has similar or better accuracy than manual calibration.

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Towards real-time time-of-arrival self-calibration using ultra-wideband anchors

Cited by 14 publications

References 22 publications

UWB anchor nodes self-calibration in NLOS conditions: a machine learning and adaptive PHY error correction approach

UWB anchor nodes self-calibration in NLOS conditions: a machine learning and adaptive PHY error correction approach

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

Calibration of Visible Light Positioning Systems with a Mobile Robot

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