Detection of Wheelchair Orientation in Human-Robot Interactions
Bibliographic record
Abstract
Autonomous mobile robots are being introduced in human-populated environments with increasing frequency, notably in hospitals and long-term care facilities. Ensuring safe and intuitive human robot interaction (HRI) is becoming a growing need, especially for pedestrians with mobility aids such as wheelchairs. The dynamics of wheelchair users differ from those of foot pedestrians, so accurate characterization of a wheelchair’s location and orientation for state estimation is crucial. The 2D laser scanner is a well-suited sensor for accurate distance measurements with fast computation speeds, but the sparsity of its data is often a hindrance to effective object detection. Despite so, 2D range data from laser scanners is found to be effective in the detection and orientation estimation of wheelchairs, even in cluttered environments. The range data from the scanner is pre-processed by segmenting out objects using density-based clustering. A two-step classification algorithm first identifies wheelchair candidates from segmented objects with the random forest classifier, then estimates the wheelchair’s orientation as one of six classes with a neural network. The models achieve 98% true positive rate for detection and 86% for orientation classification. The outcomes of this research can inform future works in building a real time wheelchair detection and state estimation for mobile robots.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".