Autonomous vision-based exploration and mapping using hybrid maps and Rao-Blackwellised particle filters
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper addresses the problem of exploring and mapping an unknown environment using a robot equipped with a stereo vision sensor. The main contribution of our work is a fully automatic mapping system that operates without the use of active ranger sensors (such as laser or sonic transducers), can operate in real-time and can consistently produce accurate maps of large-scale environments. Our approach implements a Rao-Blackwellised particle filter (RBPF) to solve the simultaneous localization and mapping problem and uses efficient data structures for real-time data association, mapping, and spatial reasoning. We employ a hybrid map representation that infers 3D point landmarks from image features to achieve precise localization, coupled with occupancy grids for safe navigation. This paper describes our framework and implementation, and presents our exploration method, and experimental results illustrating the functionality of the system
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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 | 0.000 | 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 | 0.000 | 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 it