Laser Scanner-based End-effector Tracking and Joint Variable Extraction for Heavy Machinery
Bibliographic record
Abstract
A survey of mining accidents has revealed that over 30% of all truck loading accidents can be addressed by providing dipper positioning feedback to the shovel operator. In this paper, a novel approach is presented for estimating a mining shovel’s dipper pose to obtain its arm geometry in real-time utilizing a two-dimensional laser scanner. The low spatial resolution of laser scanners and the need for accurate initialization challenge the reliability and accuracy of most laser-scanner-based object tracking methods. This work addresses these issues by using the shovel dipper’s kinematics model and position history, in conjunction with the dipper geometrical model, to track the dipper in space. The proposed method uses a bootstrap particle filter with a distance transformation in order to perform a global search in the workspace. The particle filter’s result is then used as the initial pose for an Iterative Closest Point algorithm that increases the accuracy of the pose estimate. The proposed method can be applied to other laser scanner-based object tracking applications in outdoor environments. Experiments performed on a mining shovel demonstrate the reliability, accuracy, and computational efficiency of the proposed approach. Moreover, using a single proximal sensor can simplify mounting, reduce maintenance costs and machine down time, and enhance tracking reliability.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".