Learning-based Bias Correction for Ultra-wideband Localization of Resource-constrained Mobile Robots
Bibliographic record
Abstract
Accurate indoor localization is a crucial enabling technology for many robotics applications, from warehouse management to monitoring tasks. Ultra-wideband (UWB) ranging is a promising solution which is low-cost, lightweight, and computationally inexpensive compared to alternative state-of-the-art approaches such as simultaneous localization and mapping, making it especially suited for resource-constrained aerial robots. Many commercially-available ultra-wideband radios, however, provide inaccurate, biased range measurements. In this article, we propose a bias correction framework compatible with both two-way ranging and time difference of arrival ultra-wideband localization. Our method comprises of two steps: (i) statistical outlier rejection and (ii) a learning-based bias correction. This approach is scalable and frugal enough to be deployed on-board a nano-quadcopter's microcontroller. Previous research mostly focused on two-way ranging bias correction and has not been implemented in closed-loop nor using resource-constrained robots. Experimental results show that, using our approach, the localization error is reduced by ~18.5% and 48% (for TWR and TDoA, respectively), and a quadcopter can accurately track trajectories with position information from UWB only.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".