Attitude estimation for collision recovery of a quadcopter unmanned aerial vehicle
Bibliographic record
Abstract
An extensive evaluation of attitude estimation algorithms in simulation and experiments is performed to determine their suitability for a collision recovery pipeline of a quadcopter unmanned aerial vehicle. A multiplicative extended Kalman filter (MEKF), unscented Kalman filter (UKF), complementary filter, [Formula: see text] filter, and novel adaptive varieties of the selected filters are compared. The experimental quadcopter uses a PixHawk flight controller, and the algorithms are implemented using data from only the PixHawk inertial measurement unit (IMU). Performance of the aforementioned filters is first evaluated in a simulation environment using modified sensor models to capture the effects of collision on inertial measurements. Simulation results help define the efficacy and use cases of the conventional and novel algorithms in a quadcopter collision scenario. An analogous evaluation is then conducted by post-processing logged sensor data from collision flight tests, to gain new insights into algorithms’ performance in the transition from simulated to real data. The post-processing evaluation compares each algorithm’s attitude estimate, including the stock attitude estimator of the PixHawk controller, to data collected by an offboard infrared motion capture system. Based on this evaluation, two promising algorithms, the MEKF and an adaptive [Formula: see text] filter, are selected for implementation on the physical quadcopter in the control loop of the collision recovery pipeline. Experimental results show an improvement in the metric used to evaluate experimental performance, the time taken to recover from the collision, when compared with the stock attitude estimator on the PixHawk (PX4) software.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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) | 0.001 | 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 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".