feat(planning): consolidate recorded-route localization and spatial scene

Preserve the completed teach-and-repeat laboratory stage: reference preparation, cascaded acquisition, local tracking and recovery, recording lifecycle, replay qualification, and persistent Rerun scene controls. Document the open grid-picking regression and Rerun upgrade contract. No autonomous driving or loop-closure optimization is claimed.
2026-09-21 08:47:19 +03:00
parent be58d589e2
commit e515ab1b8c
189 changed files with 19074 additions and 758 deletions
@@ -0,0 +1,75 @@
+"""Repeat the frozen negative controls with exactly the new entry search policy."""
+
+import argparse
+import json
+from pathlib import Path
+
+import numpy as np
+
+from k1link.artifacts import utc_now_iso
+from k1link.missions.causal_replay import digest
+from k1link.missions.entry_acquisition_worker import run_entry_acquisition
+
+
+def main():
+    p = argparse.ArgumentParser(description=__doc__)
+    p.add_argument("--controls", type=Path, required=True)
+    p.add_argument("--replay", type=Path, required=True)
+    p.add_argument("--output", type=Path, required=True)
+    args = p.parse_args()
+    prior = json.loads((args.controls / "report.json").read_text())
+    replay = json.loads((args.replay / "report.json").read_text())
+    path_file = args.replay / "step-003/query-path.npy"
+    files = {path_file: replay["artifacts"]["step-003/query-path.npy"]}
+    for name in ["wrong-region", "far-seed"]:
+        relative = name + "/registration-input.npz"
+        files[args.controls / relative] = prior["artifacts"][relative]
+    for file, expected in files.items():
+        if digest(file) != expected:
+            raise ValueError("Control input changed.")
+    query_path = np.load(path_file, allow_pickle=False)
+    direction = next(
+        p - query_path[0] for p in query_path[1:] if np.linalg.norm((p - query_path[0])[:2]) >= 3
+    )
+    args.output.mkdir(parents=True, exist_ok=False)
+    report = dict(
+        schema_version="missioncore.entry-controls/v1",
+        created_at_utc=utc_now_iso(),
+        source_step="step-003",
+        results={},
+        vehicle_control=False,
+        localization_confirmed=False,
+        input_digests={str(k): v for k, v in files.items()},
+    )
+    for name in ["wrong-region", "far-seed"]:
+        with np.load(args.controls / name / "registration-input.npz", allow_pickle=False) as data:
+            reference, query, initial = data["reference"], data["query"], data["initial"]
+        directory = args.output / name
+        directory.mkdir()
+        result = run_entry_acquisition(
+            directory, reference, query, initial, query_path[0], initial[:3, :3] @ direction
+        )
+        report["results"][name] = {k: v for k, v in result.items() if k != "matched_query_indices"}
+        print(
+            json.dumps(
+                dict(
+                    control=name,
+                    status=result["status"],
+                    reasons=result["reasons"],
+                    hypotheses=len(result["initialization"]["attempts"]),
+                    clusters=result["initialization"]["clusters"],
+                    elapsed_s=result["initialization"]["elapsed_s"],
+                )
+            ),
+            flush=True,
+        )
+    report["source_integrity_verified"] = all(digest(k) == v for k, v in files.items())
+    report["artifacts"] = {
+        str(x.relative_to(args.output)): digest(x) for x in args.output.rglob("*") if x.is_file()
+    }
+    report["finished_at_utc"] = utc_now_iso()
+    (args.output / "report.json").write_text(json.dumps(report, allow_nan=False))
+
+
+if __name__ == "__main__":
+    main()