Bibliographic record
Abstract
In the 21st century, we are moving ahead in making robots a ubiquitous part of our everyday life. The need for a robot to interact with the environment has become a necessity. The interaction with the world requires a sense of it's pose. Humans clearly are very good in having a sense of their location in the world around them. The same task for robots is very difficult due to the uncertainties in the movement, limitation in sensing of the environment and complexities in the environment itself. When we close our eyes and walk we have a good estimate of our location but the same can't be said for robots. Without the help of external sensors the problem of localization becomes difficult. Humans use their vestibular system to generate cues about their movement and update their position. The same can be done for robots by using acceleration, velocity or odometry as cues to a motion model. \n\nThe motion model can be represented as a distribution to account for uncertainties in the environment. The parameters to the model are typically static in the current implementation throughout the experiment. Previous work has shown that by having an online calibration method for the model has improved localization. The previous work provided a framework to build adaptive motion model and targeted land based robot and sensors.\n\nThe work presented here builds on the same framework to adapt motion models for Autonomous Underwater Vehicle. We present detailed results of the framework in a simulator. The work also proposes a method for motion estimation using side sonar images. This is used as a feedback to the motion model. We validate the motion estimation approach with real world datasets.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".