Evaluation of Human Intervention-Based Hybrid Approach for Position and Depth Estimation With Error Correction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Position and depth (PD) estimation is one of the key characteristics of autonomous robots. Robots are often challenged to visualize an alien environment remotely, alongside the control mechanism, and estimate the PD of objects. For the robot to reach out to an object, it needs to know the object’s position in a 3-D space. The design of the robot’s vision system is crucial. In this research work, we propose a human intervention-based hybrid approach for estimation of PD of an object. Human intervention in the form of a mouse click on the laser spot of the object image/in the video created by a camera-laser setup is used to estimate the PD of an object. An error correction model is developed and evaluated for better performance of the proposed method. A comparison of the proposed method with that of the image processing method revealed that a hybrid approach is almost 50% better in accuracy. The test results indicate that this method could be very helpful in finding the depth of the object with better accuracy in a teleoperated semiautonomous robot.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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