Visual Navigation for Autonomous Mobile Material Deposition Systems using Remote Sensing
Bibliographic record
Abstract
Abstract In this work, we propose a novel visual navigation method to estimate the state of mobile and fixed cold-spray material deposition systems using a stereo-camera sensor installed in the workspace. Unlike other visual localization algorithms that exploit costly onboard sensors such as LiDARs or fully rely on distinct visual cues on the robot and grid markers in the environment, our method significantly reduces the cost and complexity of the sensory setup by utilizing a cost-effective remote stereo vision system. This allows for the localization of the target system regardless of its appearance or the environment, and enables scalability for tracking and operation of multiple mobile material deposition systems at the same time. To achieve this aim, deep neural networks, kinematic constraints, and learning-aided state observers are employed to detect and estimate the location and orientation of the deposition system. A physical model of the system with bounded uncertainty and fusion with a remote visual sensing module is proposed. This accounts for frames in which depth estimation accuracy is reduced due to perceptually degraded conditions in the cold spraying context. The algorithm is evaluated on a fixed and mobile setup that demonstrate the accuracy and reliability of the proposed method.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".