Mobile Robot Localization on a Map with Large Inaccuracy.
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
A major problem in mobile robot navigation using a map is that an accurate map needs a lot of building cost. A framework of navigation using a roughly measured map would be a solution of this problem. The paper proposes a Topological-Geometrical map, or TG map for short, which permits inaccurate description, and a method of mobile robot localization on a TG map. A TG map is built by defining the relative poses between geometrical entities in the environment. The models of the geometrical entities are supposed to be predefined, and the relative poses between them can be as inaccurate as those measured by eye. These features allow the map-building cost to be small. The robot pose is represented in a local frame attached on each entity since the robot pose based on a global reference frame might be inconsistent because of the inaccuracy of the map. Errors in relative poses between entities are represented by probability density functions, and the robot pose is estimated using a variant of Markov localization which is augmented so as to incorporate map errors into data fusion process. Simulation and experiments show that the robot pose is estimated correctly on a TG map by the proposed method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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