Autonomous humanoid robot navigation using augmented reality technique
Why this work is in the frame
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Bibliographic record
Abstract
This work presents a novel vision-based navigation strategy for autonomous humanoid robots using augmented reality (AR). In the first stage, a platform is developed for indoor and outdoor human location positioning and navigation using mobile augmented reality. The image sequence would be obtained by a smart phone's camera and the location information will be provided to the user in the form of 3D graphics and audio effects containing location information. To recognize a location, an image database and location model is pre-constructed to relate the detected AR-marker's position to the map of environment. The AR-markers basically act as active landmarks placed in undiscovered environments, sending out location information once detected by a camera. The second stage implements the same algorithm on an autonomous humanoid robot to be used as its navigation module. This is achieved by coupling the robot odometry and inertial sensing with the visual marker detection module. Using this system, the robot employs its vision system to enhance its localization robustness and allow quick recovery in lost situations by detecting the active landmarks or the so called AR-markers. The problem of motion blur resulting from the 6-DOF motion of humanoid's camera is solved using an adaptive thresholding technique developed to increase the robustness of the augmented reality marker detection under different illumination conditions and camera movements. For our experiments, we used the humanoid robot NAO and verified the performance of this navigation methodology in real-world scenarios.
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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