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Use landmark_x to export each complete learned coordinate pair in first-observation order, preserving mapping and SLAM state offsets. Add native noiseless regression coverage and clarify the public map contract. AI assistance: OpenAI Codex (gpt-6.1-sol).
Up to standards ✅🟢 Issues
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| Metric | Results |
|---|---|
| Complexity | 11 |
| Duplication | 0 |
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Resolve the two actual Codacy bare-assert findings in the EKF map regression tests with equivalent numpy.testing.assert_equal checks. The focused and complete configured native suites pass. AI assistance: OpenAI Codex (gpt-6.1-sol).
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Summary
EKF.get_map()uses a landmark's first-observation ordinal as its coordinate offset. After the first landmark, exported rows combine coordinates from adjacent landmarks. For example, a learned map[(-3, 8), (7, -4), (2, 3)]is returned as[(-3, 8), (8, 7), (7, -4)]in both mapping and SLAM.Reuse
landmark_x()to obtain each coordinate pair with the existing complete-state indexing, while preserving first-observation order and a separate returned array. Add end-to-end noiseless mapping and SLAM regressions, including nonzero initial pose, different map layouts, empty and single-observation controls, and returned-array independence.Validation
968ed048639c01c9032e256565731e2703de77fe.git diff --checkpass. The existing EKF source diagnostics decrease from 8 to 7, with no new diagnostics.Checklist
pytest), with the existing skips listed aboveAI assistance: prepared with OpenAI Codex (
gpt-6.1-sol). The validation above was executed in the native environment.