Bibliographic record
Abstract
Autonomous navigation of unmanned vehicles in GPS-denied environments is a challenging problem, especially for small ground vehicles and micro aerial vehicles (MAVs) which are characterized by their small payload, short battery lifetime and limited processing resources. Stereo vision positioning has been introduced as a scale-free positioning technique, but it is computationally expensive. Monocular vision systems aided by inertial measurement unit (IMU) are more computationally efficient but it suffers from IMU random biases and scale errors. In this paper, we propose a hybrid visual-inertial odometry solution that minimizes the computation load by dividing the mission into two interchangeable stages. Firstly, a stereo vision stage in which a loosely coupled integration between stereo cameras and IMU is performed. In this stage, an extended Kalman filter (EKF) is used to automatically and dynamically estimate IMU biases. Once the IMU is calibrated, a monocular stage is activated where the system is downgraded into single camera getting the motion scale from the calibrated IMU. The proposed solution has been tested using the popular IMU-enabled ZED-Mini tracking camera. We compared our stereo vision solution against the IMU-aided monocular solution and the results showed accurate positioning with the advantage of less computation. Further analysis is provided where we compared our solution with the built-in solutions of the ZED Mini camera and the Intel Realsense T265 tracking camera.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".