Sequential Sensor Fusion for Slip Estimation in Mobile Robots
Bibliographic record
Abstract
Autonomous localization of Wheeled Mobile Robots (WMRs) in challenging extraterrestrial environments greatly depends on wheel slip estimation. In this paper, we propose a novel sequential track-to-track fusion for multi-sensor networks of unscented Kalman filters that has immediate application to wheel slip estimation of WMRs. Compared to current fusion techniques, the algorithm exhibits enhanced consistency through the propagation and utilization of cross-correlations, as well as improved computational efficiency via the implementation of a sequential scheme for fusing local estimates. As a case study, the slip estimation in a six-wheel planetary WMR with purely proprioceptive sensors is considered, where the steerable wheel sets form a network of sensors. We compare the novel slip estimator's performance with rival methods in a high-fidelity software-in-the-loop simulation and demonstrate its ability to achieve a balance between consistency, accuracy, and speed in real-time applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".