The Right Invariant Nonlinear Complementary Filter for Low Cost Attitude\n and Heading Estimation of Platforms
Bibliographic record
Abstract
This paper presents a novel filter with low computational demand to address\nthe problem of orientation estimation of a robotic platform. This is\nconventionally addressed by extended Kalman filtering of measurements from a\nsensor suit which mainly includes accelerometers, gyroscopes, and a digital\ncompass. Low cost robotic platforms demand simpler and computationally more\nefficient methods to address this filtering problem. Hence nonlinear observers\nwith constant gains have emerged to assume this role. The nonlinear\ncomplementary filter is a popular choice in this domain which does not require\ncovariance matrix propagation and associated computational overhead in its\nfiltering algorithm. However, the gain tuning procedure of the complementary\nfilter is not optimal, where it is often hand picked by trial and error. This\nprocess is counter intuitive to system noise based tuning capability offered by\na stochastic filter like the Kalman filter. This paper proposes the right\ninvariant formulation of the complementary filter, which preserves Kalman like\nsystem noise based gain tuning capability for the filter. The resulting filter\nexhibits efficient operation in elementary embedded hardware, intuitive system\nnoise based gain tuning capability and accurate attitude estimation. The\nperformance of the filter is validated using numerical simulations and by\nexperimentally implementing the filter on an ARDrone 2.0 micro aerial vehicle\nplatform.\n
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".