A Benchmark for Outdoor Vision SLAM Systems
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
Abstract Simultaneous localization and mapping (SLAM) is a viable solution to autonomous robot navigation in outdoor settings when global positioning systems are unavailable or unreliable. While the traditional exteroceptive sensor for outdoor SLAM is a laser, cameras have also been proposed due to their low power consumption, low price, high bandwidth of information, and superior landmark segmentation capabilities. All outdoor Vision SLAM systems developed to date are implemented on different platforms, in different settings, using different dead‐reckoning sensors; a fact which makes it difficult to compare them and assess the state of the art of Vision SLAM. The contribution of this paper is in developing an infrastructure for a benchmark upon which past and future Vision SLAM system can be compared. This proposed benchmark is validated by testing its datasets on a Vision‐Inertial SLAM system. © 2007 Wiley Periodicals, Inc.
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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