Indoor Navigation based on an Inaccurate Map using Object Recognition
Why this work is in the frame
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Bibliographic record
Abstract
Although an environment map provides essential information for mobile robot navigation, an accurate map needs a lot of building cost and is not flexible to changes in object poses in the environment. A solution of these problems would be a framework of navigation using an inaccurate map. We have already proposed a representation of an inaccurate map and a localization method on the proposed map. In this paper, we present an object-recognition method suitable for mobile robot localization, and a path-tracking method on the proposed map. The robot recognizes objects such as desk and door, and localizes itself based on the relative poses from the objects. Since a path may also be inaccurate on an inaccurate map, the robot corrects the path based on localization results while tracking the path. Integrating these methods, we have built an indoor navigation system. An experiment shows that the robot successfully navigated in an indoor environment, recognizing several kinds of objects.
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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