MOBILE STEREO-MAPPER: A PORTABLE KIT FOR UNMANNED AERIAL VEHICLES
Bibliographic record
Abstract
Abstract. A low-cost portable light-weight mobile stereo-mapping system (MSMS) is under development in the GeoICT Lab, Geomatics Engineering program at York University. The MSMS is designed for remote operation on board unmanned aerial vehicles (UAV) for navigation and rapid collection of 3D spatial data. Pose estimation of the camera sensors is based on single frequency RTK-GPS, loosely coupled in a Kalman filter with MEMS-based IMU. The attitude and heading reference system (AHRS) calculates orientation from the gyro data, aided by accelerometer and magnetometer data to compensate for gyro drift. Two low-cost consumer digital cameras are calibrated and time-synchronized with the GPS/IMU to provide direct georeferenced stereo vision, while a video camera is used for navigation. Object coordinates are determined using rigorous photogrammetric solutions supported by direct georefencing algorithms for accurate pose estimation of the camera sensors. Before the MSMS is considered operational its sensor components and the integrated system itself has to undergo a rigorous calibration process to determine systematic errors and biases and to determine the relative geometry of the sensors. In this paper, the methods and results for system calibration, including camera, boresight and leverarm calibrations are presented. An overall accuracy assessment of the calibrated system is given using a 3D test field.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.048 | 0.021 |
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".