Directional Endpoint-based Enhanced EKF-SLAM for Indoor Mobile Robots
Bibliographic record
Abstract
This paper proposes an enhanced Extended Kalman Filter (EKF)-based Simultaneous Localization and Mapping (SLAM) algorithm based on `directional endpoint' features extracted from two-dimensional (2D) laser data for indoor environments. The proposed approach is composed of calculating the covariances of the extracted line segments, calculating the covariances of the directional endpoints, and enhanced EKF-SLAM. Different from the classical SLAM based on point and line features, this work uses the directional endpoint feature, which has 3 degrees of freedom. To facilitate the enhanced EKF-SLAM, the implicit function theorem and the geometrical method are used to obtain the uncertainty of the directional endpoint. Comparative experimental results show superior performance of our proposed algorithm. In addition, the enhanced EKF-SLAM achieves the similar performance compared with Karto-SLAM in terms of pose estimation, but at the same time, the feature map composed of a set of directional endpoints is obtained, which is robust in dynamic environments.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".