Mobile Robot Motion Tracking Using Descriptor Matching and Sensor Fusion
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents fast tracking of a mobile robots 2D pose in a plane using the open source computer vision library(OpenCV). This can be useful for setting up experiments to study mobile robot control, robot formation or conflict resolution. Here the feature detectors SIFT, AKAZE and ORB are tested for their speed and accuracy for tracking a robot on a plane of size 2.7m × 2.1m. To determine the accuracy that can be achieved they are compared against an edge-based template matching algorithm which has a known accuracy. First the accuracy vs detection time is studied on different size images. Then sensor fusion is studied by combining the extended Kalman filter (EKF) and unscented Kalman filter (UKF) with odometry to see what gains can be made. Root mean squared pose errors of less than 3mm in translation and less than 1 degree in heading are achieved at a object detection times of less than 50ms.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it