Global Visual and Semantic Observations for Outdoor Robot Localization
Bibliographic record
Abstract
Most approaches to robot visual localization rely on local, global visual or semantic information as observation. In this paper, the combination of global visual and semantic information is used as landmark in the observation model of Bayesian filters. Introducing the improved Gaussian Process into observation models with visual information, The GP-Localize algorithm is extended to high dimensional data, which means that all the historical data with spatiotemporal correlation to achieve constant time and memory for persistent outdoor robot localization can be considered by the Bayesian filters. The other contribution of this paper is we combine the all above parts into a system for robot visual localization and apply it to two real-world outdoor datasets including unmanned ground vehicle (UGV) and unmanned aerial vehicle (UAV). According to the experimental results, it's no difficult to find there is a higher accuracy while using global vision and semantic results rather than just using single feature. By using the combined features and improved Gaussian process approximation method in Bayes filters, our system is more robust and practical than existing localization systems such as ORB-SLAM.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".