A teaching tool for the state‐of‐the‐art probabilistic methods used in localization of mobile robots
Bibliographic record
Abstract
Abstract Probabilistic methods provide a powerful paradigm for modeling of robot motion and its environment; a precursor for autonomous navigation of mobile robots. As a graduate‐level engineering course, probabilistic robotics encompasses the techniques used in robot localization and mapping in unstructured environments. This article presents a simulation and animation software program developed mainly as a teaching tool that can help the students visualize different robot localization solutions through both parametric filters (viz., the Extended Kalman Filter, Unscented Kalman Filter) and nonparametric filters (viz., the histogram filter, Rao‐Blackwell particle filter). The program is also a powerful tool for performance analysis and tuning of such filters commonly used for robot localization, mapping, and autonomous navigation. The simulation features dead reckoning (e.g., INS), range‐only sensing (e.g., rangefinder), bearing‐only sensing (e.g., digital camera), or a combination of them. The program includes a simple graphical user interface that allows for changing both filtering and sensing parameters, and monitoring the effects of those changes on the animation of a unicycle in a 2D environment. The program animates the unicycle motion and shows the kinematic results in several graphs simultaneously to evaluate the performance of different methods in finding the robot pose. The program is available as an open‐source Matlab script to facilitate future modifications and improvements of the code by the students interested in robotics, mechatronics, and control engineering. This article presents the features of the program and briefly discusses the algorithms implemented in the software. © 2010 Wiley Periodicals, Inc. Comput Appl Eng Educ 20: 721–727, 2012
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.076 | 0.023 |
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".