Robust and efficient multi-robot 3D mapping with octree based occupancy grids
Bibliographic record
Abstract
A technique for merging 3D octree based occupancy grid maps robust to error in transformation between map reference frames is proposed and implemented. Recent robotics applications require 3D representations of the environments in which robots are to operate. In many cases, such as Simultaneous Localization and Mapping (SLAM) and when a large environment is required to be mapped within a reasonable time constraint, it is not feasible for a single robot to map the entire portion of the environment required to be mapped. In these cases it is necessary for a team of robots to build maps independently and merge them into a single global map. The contribution of this work lies in the introduction of methods which use map data from commonly mapped portions of the environment with registration techniques such that maps may still be merged coherently despite erroneous relative transformations between maps. The results from this paper demonstrate that not only are octree occupancy grids a suitable representation for multi-robot 3D mapping, but that the proposed techniques for improving erroneous transformation estimates between map frames are valid.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".