Iterative Bias Estimation for an Ultra-Wideband Localization System

Heijden, Bas van der; Ledergerber, Anton; Gill, Rajan; D’Andrea, Raffaello

doi:10.1016/j.ifacol.2020.12.1889

Cited by 9 publications

(6 citation statements)

References 12 publications

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“…The proposed method in [14] learns a Gaussian process noise model without ground truth information, but still requires training data to learn the model beforehand. The iterative measurement bias learning approach proposed in [15] does not have an offline training step, but requires repeating the same trajectory multiple times to learn the bias model. More generic mechanisms to reduce the influence of the biases and measurement outliers are M-estimators [16], which apply robust cost functions to downweight large measurement residuals.…”

Section: Related Workmentioning

confidence: 99%

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Finding the Right Place: Sensor Placement for UWB Time Difference of Arrival Localization in Cluttered Indoor Environments

Zhao

Goudar

Schoellig

2022

IEEE Robot. Autom. Lett.

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Ultra-wideband (UWB) time difference of arrival (TDOA)-based localization has recently emerged as a promising indoor positioning solution. However, in cluttered environments, both the UWB radio positions and the obstacle-induced nonline-of-sight (NLOS) measurement biases significantly impact the quality of the position estimate. Consequently, the placement of the UWB radios must be carefully designed to provide satisfactory localization accuracy for a region of interest. In this work, we propose a novel algorithm that optimizes the UWB radio positions for a pre-defined region of interest in the presence of obstacles. The mean-squared error (MSE) metric is used to formulate an optimization problem that balances the influence of the geometry of the radio positions and the NLOS effects. We further apply the proposed algorithm to compute a minimal number of UWB radios required for a desired localization accuracy and their corresponding positions. In a realworld cluttered environment, we show that the designed UWB radio placements provide 47% and 76% localization root-meansquared error (RMSE) reduction in 2D and 3D experiments, respectively, when compared against trivial placements.

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Section: Related Workmentioning

confidence: 99%

“…In cluttered environments, the UWB measurement errors {η ij,tn } often show biased and non-Gaussian distributions due to degraded radio signals caused by NLOS and multipath radio propagation [8], [15]. We use Gaussian mixture models (GMMs) to model those distributions due to their flexibility.…”

Section: B Gaussian Mixture Model For Nonlinear Least Squaresmentioning

confidence: 99%

Finding the Right Place: Sensor Placement for UWB Time Difference of Arrival Localization in Cluttered Indoor Environments

Zhao

Goudar

Schoellig

2022

IEEE Robot. Autom. Lett.

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“…In [19] additional sensors such as lidars are used. The methods proposed in [12], [23] use training data collected from multiple closed-loop flights to learn the bias models. A variety of models including parametric models [3], [12], [14] and non-parametric models such as Gaussian processes [21], neural networks [22], and kernel density-based models [24] have been used to estimate the biases in range measurements.…”

Section: Related Workmentioning

confidence: 99%

“…The methods proposed in [12], [23] use training data collected from multiple closed-loop flights to learn the bias models. A variety of models including parametric models [3], [12], [14] and non-parametric models such as Gaussian processes [21], neural networks [22], and kernel density-based models [24] have been used to estimate the biases in range measurements. Some of the limitations of previous works are that they require accurate ground truth information or training data and can be computationally intractable for online estimation.…”

Section: Related Workmentioning

confidence: 99%

“…Filtering-based methods [5], [10], [11], while computationally lightweight, can result in poorer accuracy and non-smooth estimates as they discard older measurements. These non-smooth estimates can result in oscillatory behavior when used for online control [12]. In this paper, we propose a sensor fusion architecture that meets all of the above requirements.…”

Section: Introductionmentioning

confidence: 99%

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Gaussian Variational Inference with Covariance Constraints Applied to Range-only Localization

Goudar

Zhao

Barfoot

et al. 2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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We propose a fixed-lag smoother-based sensor fusion architecture to leverage the complementary benefits of rangebased sensors and visual-inertial odometry (VIO) for localization. We use two fixed-lag smoothers (FLS) to decouple accurate state estimation and high-rate pose generation for closed-loop control. The first FLS combines ultrawideband (UWB)-based range measurements and VIO to estimate the robot trajectory and any systematic biases that affect the range measurements in cluttered environments. The second FLS estimates smooth corrections to VIO to generate pose estimates at a high rate for online control. The proposed method is lightweight and can run on a computationally constrained micro-aerial vehicle (MAV). We validate our approach through closed-loop flight tests involving dynamic trajectories in multiple real-world cluttered indoor environments. Our method achieves decimeter-to-subdecimeter-level positioning accuracy using off-the-shelf sensors and decimeter-level tracking accuracy with minimally-tuned open-source controllers.

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Distributed Initialization for Visual-Inertial-Ranging Odometry with Position-Unknown UWB Network

Jia

Xiong

Wang

2023

2023 IEEE International Conference on Robotics and Automation (ICRA)

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Iterative Bias Estimation for an Ultra-Wideband Localization System

Cited by 9 publications

References 12 publications

Finding the Right Place: Sensor Placement for UWB Time Difference of Arrival Localization in Cluttered Indoor Environments

Finding the Right Place: Sensor Placement for UWB Time Difference of Arrival Localization in Cluttered Indoor Environments

Gaussian Variational Inference with Covariance Constraints Applied to Range-only Localization

Distributed Initialization for Visual-Inertial-Ranging Odometry with Position-Unknown UWB Network

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