Visual Inertial SLAM: Application to Unmanned Aerial Vehicles
Bibliographic record
Abstract
Unmanned Aerial Vehicles (UAVs) require an accurate estimate of their state. Computer vision provides a number of benefits over conventionally used sensors, such as the Global Positioning System (GPS) or a Motion Capture System (MCS), in order to achieve state estimation and localization relative to a scene. Our work uses the output of an existing Visual Simultaneous Localization and Mapping (VSLAM) system which provides a scaled position measurement. We propose an observer design to estimate vehicle position and linear velocity. The observer fuses an accelerometer measurement from an Inertial Measurement Unit (IMU) and VSLAM system output. The observer depends on an attitude estimate from an Attitude and Heading Reference System (AHRS). A change of coordinates is used to transform the system into a Linear Time-Varying (LTV) form. Using these coordinates we consider the observability of the Visual Inertial Simultaneous Localization and Mapping (VISLAM) problem. Two observer designs are proposed and their performance is validated in simulation and experiment.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".