Robust and Efficient Multirobot 3-D Mapping Merging With Octree-Based Occupancy Grids
Bibliographic record
Abstract
Recent robotics applications require 3-D representations of the environments. In many cases, it is not feasible for a single robot to map the entire environment. In these cases, it is necessary for a team of robots to build maps independently and merge them into a single global map. In this paper, octree-based occupancy grids, which are currently the state-of-the-art 3-D map representation, are applied to the problem of multirobot mapping. Octrees allow large environments to be mapped efficiently, in terms of memory usage, while still providing sufficiently fine resolution where required. The main contribution of this work lies in the definition and validation of a system, which use map data from commonly mapped portions of the environment with registration techniques, such that maps are merged coherently despite measurement noise and error in the relative transformations between maps for experimental data sets. The system defined can then be used in a complete solution that is ported to mobile robots. The results demonstrate that octree occupancy grids are a suitable representation for multirobot 3-D mapping, but that the proposed techniques for improving erroneous transformation estimates between map frames allow multiple maps to be merged efficiently and robustly.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".