Vision-based Exploration Algorithms for Rough Terrain Modeling Using Triangular Mesh Maps
Bibliographic record
Abstract
The purpose of this paper is to develop a new exploratory approach based on a triangular mesh map for automatic modeling of a large rough agricultural environment. A triangular mesh map was used to represent the agricultural field surface because of its ability to represent large rough areas efficiently. A terrain map is built incrementally during exploration, using 3D image sensor readings. A 3D image sensor model, with attributes similar to a camera or laser sensor, was used in the simulation. A two-stage exploring policy was used to plan the next-best view by considering both the distance and elevation change in the cost function. In the first stage of exploration, the robot travels to the outer boundary between the explored and unexplored terrain, while in the second stage it fills in the hole left by the first stage. Previous work considered distance as the only traveling cost. In this work, the slope factor is also included in the cost function because the mobile robot needs more energy to overcome the changes in elevation. A line sweeping approach based on the bug concept is also presented to identify a path for complete coverage of the terrain. The two methods are implemented and validated in simulation. A complete comparison of the traveling distance, time consumption, and number of scans recorded using the two methods is presented to show the effectiveness of the two-stage exploration algorithm.
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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 | 0.002 | 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 | 0.001 |
| Open science | 0.001 | 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".