Approximate Inference Particle Filtering for Mobile Robot SLAM
Bibliographic record
Abstract
This paper proposes approximate inference particle filtering for mobile robot simultaneous localization and mapping (SLAM) with landmarks. Range-bearing measurements are obtained by detecting landmarks using an onboard laser range finder and the maximum likelihood approach is used to handle unknown data associations. The system model is created depending on the robot motion and range-bearing measurements. The new particle filter is developed to estimate the robot pose and landmark locations separately based on approximate inferences, where the intractable distributions of the robot pose and landmark locations are approximated by optimal Gaussian distributions minimizing Kullback-Leibler divergences. Simulations and experiments are provided to show the performance of the proposed particle filter. Note to Practitioners—The SLAM technique is crucial to an autonomous mobile robot. The laser range finder perceives surroundings and is used for mobile robot SLAM in global positioning system denied environments. This paper proposes a mobile robot SLAM method based on the robot motion model and the onboard laser range finder. Range-bearing measurements are extracted from raw data of the laser range finder as the inputs of the method. The simulations and experiments indicate the proposed method has better accuracy and more robust to unknown data associations. The proposed method can be applied to many practical applications such as transport operations and mowing with a mobile robot. In the future, we will address the SLAM technique for multiple mobile robots.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".