Directional Endpoint-based Enhanced EKF-SLAM for Indoor Mobile Robots
Why this work is in the frame
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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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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) | 0.001 | 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 it