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Efficient Planar Pose Estimation via UWB Measurements
Robotics · Submitted 14 Sep 2022
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim GN-ULS attains millimeter and sub-degree level accuracy on our static datasets and attains centimeter and degree level accuracy on our dynamic datasets, presenting the possibility of using only UWB for real-time state estimation.
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The abstract, sentence by sentence
State estimation is an essential part of autonomous systems. Integrating the Ultra-Wideband(UWB) technique has been shown to correct the long-term estimation drift and bypass the complexity of loop closure detection. However, few works on robotics adopt UWB as a stand-alone state estimation solution. The primary purpose of this work is to investigate planar pose estimation using only UWB range measurements and study the estimator's statistical efficiency. We prove the excellent property of a two-step scheme, which says that we can refine a consistent estimator to be asymptotically efficient by one step of Gauss-Newton iteration. Grounded on this result, we design the GN-ULS estimator and evaluate it through simulations and collected datasets. GN-ULS attains millimeter and sub-degree level accuracy on our static datasets and attains centimeter and degree level accuracy on our dynamic datasets, presenting the possibility of using only UWB for real-time state estimation.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | State estimation is an essential part of autonomous systems. | background 1.00 | 0.04 | 0.01 | asserted 1.00 |
| s1 | Integrating the Ultra-Wideband(UWB) technique has been shown to correct the long-term estimation drift and bypass the complexity of loop closure detection. | background 1.00 | 0.07 | 0.04 | asserted 1.00 |
| s2 | However, few works on robotics adopt UWB as a stand-alone state estimation solution. | gap 0.99 | 0.07 | 0.02 | asserted 0.99 |
| s3 | The primary purpose of this work is to investigate planar pose estimation using only UWB range measurements and study the estimator's statistical efficiency. | contribution 1.00 | 0.44 | 0.05 | asserted 0.84 |
| s4 | We prove the excellent property of a two-step scheme, which says that we can refine a consistent estimator to be asymptotically efficient by one step of Gauss-Newton iteration. | contribution 0.63 | 0.88 | 0.13 | asserted 1.00 |
| s5 | Grounded on this result, we design the GN-ULS estimator and evaluate it through simulations and collected datasets. | method 0.71 | 0.95 | 0.06 | asserted 0.97 |
| s6 | GN-ULS attains millimeter and sub-degree level accuracy on our static datasets and attains centimeter and degree level accuracy on our dynamic datasets, presenting the possibility of using only UWB for real-time state estimation. | result 0.97 | 0.96 | 0.85 | asserted 0.63 |
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Original abstract
State estimation is an essential part of autonomous systems. Integrating the Ultra-Wideband(UWB) technique has been shown to correct the long-term estimation drift and bypass the complexity of loop closure detection. However, few works on robotics adopt UWB as a stand-alone state estimation solution. The primary purpose of this work is to investigate planar pose estimation using only UWB range measurements and study the estimator's statistical efficiency. We prove the excellent property of a two-step scheme, which says that we can refine a consistent estimator to be asymptotically efficient by one step of Gauss-Newton iteration. Grounded on this result, we design the GN-ULS estimator and evaluate it through simulations and collected datasets. GN-ULS attains millimeter and sub-degree level accuracy on our static datasets and attains centimeter and degree level accuracy on our dynamic datasets, presenting the possibility of using only UWB for real-time state estimation.
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We estimate each tag's position using the Bias-Eli-Lin estimator proposed in [ref] , which is $√(M_T)-$consistent and with $O(M_T)$ computational complexity in this paper's notation.
Global and Asymptotically Efficient Localization from Range Measurements · Divide-and-Conquer Estimator · 1.00
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