feat(lab): publish fail-closed TGS evidence

apps/control-station/src/workspaces/laboratory/AdvancedLaboratoryResult.tsx

@@ -48,6 +48,7 @@ import { M48StaticOccupancyQualificationResultView } from "./M48StaticOccupancyQ
 import { M48R3StaticOccupancyShadowResultView } from "./M48R3StaticOccupancyShadowResult";
 import { M48SFixedClassDetectorResultView } from "./M48SFixedClassDetectorResult";
 import { M48TRiskQualityResultView } from "./M48TRiskQualityResult";
+import { M49TgsFailClosedResultView } from "./M49TgsFailClosedResult";
 
 export { isAdvancedLaboratoryWorkId };
 export type { AdvancedLaboratoryWorkId };
@@ -108,6 +109,9 @@ export function AdvancedLaboratoryResult({
   if (workId === "m48t-risk-quality-temporal" && results.m48t) {
     return <M48TRiskQualityResultView rigLabel={rigLabel} result={results.m48t} />;
   }
+  if (workId === "m49-tgs-fail-closed-evidence" && results.m49Tgs) {
+    return <M49TgsFailClosedResultView rigLabel={rigLabel} result={results.m49Tgs} />;
+  }
   if (workId === "m47-reference-graph-shadow" && results.m47Graph) {
     return <M47ReferenceGraphResultView rigLabel={rigLabel} result={results.m47Graph} />;
   }

apps/control-station/src/workspaces/laboratory/M49TgsFailClosedEvidence.tsx

@@ -0,0 +1,152 @@
+import { useCallback, useEffect, useMemo, useRef, useState } from "react";
+
+import type {
+  RecordedEvidenceSemanticClass,
+  RecordedEvidenceSemanticPaletteEntry,
+} from "../../components/laboratory/RecordedEvidenceSemanticMaskOverlay";
+import {
+  fetchM49TgsAnchorSpatial,
+  type M49TgsAnchorSpatial,
+  type M49TgsFailClosedResult,
+  type M49TgsStateCode,
+} from "../../core/laboratory/m49TgsFailClosed";
+import {
+  M4ReplayThreatVisual,
+  type M4ReplayClassifiedSpatialFrame,
+  type M4ReplayThreatReviewAnchor,
+} from "./M4ReplayThreatVisual";
+
+const CLASSES: readonly RecordedEvidenceSemanticClass[] = [
+  { id: 1, label: "Ground support" },
+  { id: 2, label: "Non-ground occupied" },
+  { id: 3, label: "Unknown / rejected" },
+];
+
+const PALETTE: readonly RecordedEvidenceSemanticPaletteEntry[] = [
+  { classId: 1, color: { kind: "token", token: "--nodedc-success-rgb" } },
+  { classId: 2, color: { kind: "token", token: "--nodedc-danger-rgb" } },
+  { classId: 3, color: { kind: "token", token: "--nodedc-warning-rgb" } },
+];
+
+function cellState(code: M49TgsStateCode): M4ReplayClassifiedSpatialFrame["cellsMapGravityLocal"][number]["state"] {
+  if (code === 1) return "ground-support";
+  if (code === 2) return "nonground-occupied";
+  if (code === 3) return "unknown-rejected";
+  return "unobserved";
+}
+
+function message(error: unknown): string {
+  return error instanceof Error && error.message.trim()
+    ? error.message
+    : "M49 TGS spatial evidence недоступно.";
+}
+
+export function M49TgsFailClosedEvidence({
+  result,
+}: {
+  result: M49TgsFailClosedResult;
+}) {
+  const [activeSequence, setActiveSequence] = useState<number | null>(null);
+  const [spatial, setSpatial] = useState<M49TgsAnchorSpatial | null>(null);
+  const [loading, setLoading] = useState(false);
+  const [error, setError] = useState<string | null>(null);
+  const spatialCacheRef = useRef(new Map<string, M49TgsAnchorSpatial>());
+  const anchorSequences = useMemo(
+    () => new Set(result.metrics.primary.map((item) => item.anchorFrameIndex)),
+    [result.metrics.primary],
+  );
+  const expectedAtSequence = activeSequence !== null && anchorSequences.has(activeSequence);
+
+  useEffect(() => {
+    if (activeSequence === null || !anchorSequences.has(activeSequence)) {
+      setSpatial(null);
+      setLoading(false);
+      setError(null);
+      return;
+    }
+    const cacheKey = `${result.resultId}:${activeSequence}`;
+    const cached = spatialCacheRef.current.get(cacheKey);
+    if (cached) {
+      setSpatial(cached);
+      setLoading(false);
+      setError(null);
+      return;
+    }
+    const controller = new AbortController();
+    setSpatial(null);
+    setLoading(true);
+    setError(null);
+    void fetchM49TgsAnchorSpatial(
+      result.resultId,
+      activeSequence,
+      "causal_rolling_1s",
+      { signal: controller.signal },
+    )
+      .then((next) => {
+        if (!controller.signal.aborted) {
+          spatialCacheRef.current.set(cacheKey, next);
+          setSpatial(next);
+        }
+      })
+      .catch((caught: unknown) => {
+        if (!controller.signal.aborted) setError(message(caught));
+      })
+      .finally(() => {
+        if (!controller.signal.aborted) setLoading(false);
+      });
+    return () => controller.abort();
+  }, [activeSequence, anchorSequences, result.resultId]);
+
+  const reviewAnchors = useMemo<readonly M4ReplayThreatReviewAnchor[]>(
+    () => result.metrics.primary.map((item) => ({
+      id: `m49-tgs-${item.anchorFrameIndex}`,
+      sourceSequence: item.anchorFrameIndex,
+      extentXyxyNormalized: [0, 0, 0, 0],
+      matchedAtThreshold: false,
+      statusLabel: "визуальная проверка",
+    })),
+    [result.metrics.primary],
+  );
+
+  const classifiedFrame = useMemo<M4ReplayClassifiedSpatialFrame | null>(() => {
+    if (!spatial) return null;
+    return {
+      sourceSequence: spatial.sourceSequence,
+      pointsMapGravityLocalXyzM: spatial.pointsXyzM,
+      pointClassIds: spatial.pointStates,
+      cellsMapGravityLocal: spatial.costmap.centersXyM.map((center, index) => ({
+        centerXyM: center,
+        zBoundsM: spatial.costmap.zBoundsM[index]!,
+        state: cellState(spatial.costmap.states[index]!),
+      })),
+      cellSizeM: spatial.costmap.cellSizeM,
+      classes: CLASSES,
+      palette: PALETTE,
+    };
+  }, [spatial]);
+
+  const handleSequenceChange = useCallback((sequence: number | null) => {
+    setActiveSequence(sequence);
+  }, []);
+
+  return (
+    <M4ReplayThreatVisual
+      resultId={result.source.linkedVisualResultId}
+      reviewAnchors={reviewAnchors}
+      showReviewAnchorBoxes={false}
+      reviewLabel="10 gravity-aligned TGS anchors"
+      evidenceLabel="M49 · TGS fail-closed"
+      initialSpatialMode="3d"
+      onActiveSequenceChange={handleSequenceChange}
+      classifiedSpatialLayer={{
+        label: "TGS fail-closed · causal rolling 1 s",
+        pointLayerLabel: "TGS POINTS",
+        cellLayerLabel: "COSTMAP",
+        expectedAtSequence,
+        frame: classifiedFrame,
+        loading,
+        error,
+      }}
+    />
+  );
+}

apps/control-station/src/workspaces/laboratory/M49TgsFailClosedResult.tsx

@@ -0,0 +1,92 @@
+import {
+  LaboratoryEvidence,
+  LaboratoryResultSummary,
+  LaboratorySummary,
+  LaboratoryWorkTemplate,
+} from "../../components/laboratory/LaboratoryPresentation";
+import type { M49TgsFailClosedResult } from "../../core/laboratory/m49TgsFailClosed";
+import { M49TgsFailClosedEvidence } from "./M49TgsFailClosedEvidence";
+
+function number(value: number, digits = 1): string {
+  return value.toLocaleString("ru-RU", { maximumFractionDigits: digits });
+}
+
+export function M49TgsFailClosedResultView({
+  rigLabel,
+  result,
+}: {
+  rigLabel: string;
+  result: M49TgsFailClosedResult;
+}) {
+  const worst = [...result.metrics.primary].sort(
+    (left, right) => (
+      right.nongroundPointCount / Math.max(right.pointCount, 1)
+      - left.nongroundPointCount / Math.max(left.pointCount, 1)
+    ),
+  )[0]!;
+  const status = "Representation complete; визуальное качество ещё не принято";
+  return (
+    <LaboratoryWorkTemplate
+      summary={(
+        <LaboratorySummary
+          title="M4.9T4 · TRAVEL TGS fail-closed evidence"
+          description="TRAVEL GroundSeg запущен без AOS на десяти immutable RAVNOVES00 anchors. Вход сохранён в gravity-aligned map frame; каждый eligible point получил состояние, а каждая costmap-ячейка остаётся ground, occupied, rejected или unobserved."
+          status={status}
+          statusTone="warning"
+          facts={[
+            { label: "Конфигурация", value: `${rigLabel} RIGHT · Camera + gravity-aligned LiDAR · 10 anchors` },
+            { label: "Метод", value: "TRAVEL TGS only · AOS OFF · causal rolling 1 s" },
+            { label: "Evidence", value: `${result.metrics.anchorCount} anchors · ${result.metrics.costmapCellCount.toLocaleString("ru-RU")} cells/anchor · all points accounted` },
+            { label: "Нагрузка", value: `Worker 006 CPU-only · GPU 0 · wrapper ${number(result.execution.wrapperElapsedSeconds, 2)} с` },
+            { label: "Authority", value: "REPLAY-SIMULATED · visual/traversability/navigation/actuation OFF" },
+          ]}
+          brief={{
+            question: "Отделяет ли готовый TRAVEL TGS опорную поверхность от неизвестной занятой геометрии достаточно чисто, чтобы заменить самодельный static-obstacle threshold pipeline?",
+            approach: "На десяти сложных кадрах проверяется полный gravity-aligned point set и fail-closed costmap. Зелёное — опора, красное — non-ground occupied, жёлтое — rejected/unknown, тёмное — unobserved; камера остаётся синхронным первичным контекстом.",
+            principalResult: `Контракт представления закрыт: ${result.metrics.anchorProfileCount}/20 профилей, ни одной потерянной eligible point, AOS и GPU отсутствуют. Process wall rolling p50/max: ${number(result.metrics.processWallRollingP50Ms, 0)}/${number(result.metrics.processWallRollingMaxMs, 0)} мс.`,
+            limitation: `Качество не принято: особенно проверить кадр ${worst.anchorFrameIndex + 1}, где ${number(worst.nongroundPointCount / Math.max(worst.pointCount, 1) * 100)}% rolling points помечены non-ground. Это может быть реальная боковая геометрия либо ложная блокировка поверхности.`,
+          }}
+          method={{
+            completeness: "complete",
+            executionClass: "deterministic",
+            pipelineId: "travel-tgs-gravity-aligned-fail-closed/v1",
+            components: [
+              { kind: "source", name: "RAVNOVES00", version: "10 immutable anchors", role: "camera + registered map increments", identitySha256: result.source.sourcePackSha256 },
+              { kind: "algorithm", name: "TRAVEL GroundSeg", version: "95dc2fbd66a343efd9060c45a5711b6307a950a4", role: "ground/nonground separation; AOS excluded", identitySha256: result.configuration.configSha256 },
+              { kind: "algorithm", name: "fail-closed complement adapter", version: "v1", role: "explicit rejected points and unobserved cells", identitySha256: result.resultId.split("-").at(-1) ?? null },
+            ],
+          }}
+        />
+      )}
+      evidence={(
+        <LaboratoryEvidence
+          eyebrow="M4.9T4 VISUAL EVIDENCE · CAMERA + GRAVITY-ALIGNED TGS"
+          title="10 anchors: полный TGS point set и четырёхсостояний costmap на том же recorded timeline"
+          kind="recorded-replay"
+          resizable
+        >
+          <M49TgsFailClosedEvidence result={result} />
+        </LaboratoryEvidence>
+      )}
+      result={(
+        <LaboratoryResultSummary
+          title="Что уже доказано и что проверяем глазами"
+          status={status}
+          statusTone="warning"
+          metrics={[
+            { label: "Point accounting", value: "100%", hint: "ground + non-ground + rejected = exact eligible input" },
+            { label: "Anchors", value: `${result.metrics.anchorCount}/10`, hint: "current + causal rolling 1 s" },
+            { label: "Costmap", value: `${result.metrics.costmapCellCount.toLocaleString("ru-RU")} cells`, hint: `${number(result.configuration.cellSizeM, 2)} м · radius ${number(result.configuration.radiusM, 0)} м` },
+            { label: "Process wall rolling", value: `${number(result.metrics.processWallRollingP50Ms, 0)} / ${number(result.metrics.processWallRollingMaxMs, 0)} мс`, hint: "p50 / max · CPU process envelope, не realtime integration" },
+            { label: "GPU / AOS", value: "0 / OFF", hint: "Worker 006; Frigate budget не затронут" },
+          ]}
+          conclusion={{
+            proved: "Готовый TGS можно встроить fail-closed: исходные точки не теряются, unknown не становится free, AOS не нужен, а вычисление укладывается в лёгкий CPU-контур на этих anchors.",
+            notProved: "Не доказано, что красный non-ground слой не режет дорогу, траву или допустимые просветы. Нет независимой terrain truth, полного replay, realtime graph integration и модели корпуса.",
+            decision: "Открыть десять anchors по очереди. Если красное остаётся на реальных препятствиях и не перекрывает видимую опорную поверхность, TGS идёт в полный shadow; иначе кандидат отклоняется без ручной подгонки порогов под эти кадры.",
+          }}
+        />
+      )}
+    />
+  );
+}

apps/control-station/src/workspaces/laboratory/M4ReplayThreatVisual.tsx

@@ -1,4 +1,4 @@
-import { useEffect, useMemo, useRef, useState, type CSSProperties } from "react";
+import { useCallback, useEffect, useMemo, useRef, useState, type CSSProperties } from "react";
 import {
   Button,
   Icon,
@@ -12,6 +12,7 @@ import {
 import { ObservationTimeline } from "../../components/ObservationTimeline";
 import {
   LaboratoryMetricEvidenceScene,
+  type LaboratoryMetricCellEvidence,
   type LaboratoryMetricEvidenceSceneHandle,
   type LaboratoryMetricSceneMode,
 } from "../../components/laboratory/LaboratoryMetricEvidenceScene";
@@ -103,6 +104,31 @@ export interface M4ReplayThreatReviewAnchor {
   sourceSequence: number;
   extentXyxyNormalized: readonly [number, number, number, number];
   matchedAtThreshold: boolean;
+  statusLabel?: string;
+}
+
+export interface M4ReplayClassifiedSpatialFrame {
+  sourceSequence: number;
+  pointsMapGravityLocalXyzM: readonly (readonly [number, number, number])[];
+  pointClassIds: readonly (number | null)[];
+  cellsMapGravityLocal: readonly {
+    centerXyM: readonly [number, number];
+    zBoundsM: readonly [number | null, number | null];
+    state: LaboratoryMetricCellEvidence["state"];
+  }[];
+  cellSizeM: number;
+  classes: readonly RecordedEvidenceSemanticClass[];
+  palette: readonly RecordedEvidenceSemanticPaletteEntry[];
+}
+
+export interface M4ReplayClassifiedSpatialLayer {
+  label: string;
+  pointLayerLabel: string;
+  cellLayerLabel: string;
+  expectedAtSequence: boolean;
+  frame: M4ReplayClassifiedSpatialFrame | null;
+  loading: boolean;
+  error: string | null;
 }
 
 const EMPTY_REVIEW_ANCHORS: readonly M4ReplayThreatReviewAnchor[] = [];
@@ -115,6 +141,9 @@ export function M4ReplayThreatVisual({
   reviewLabel = "Контрольные примеры M4.8R1",
   timelineEndpointRoot,
   evidenceLabel = "M4.6",
+  initialSpatialMode = null,
+  classifiedSpatialLayer,
+  onActiveSequenceChange,
 }: {
   resultId: string;
   semantic?: M4ReplayThreatSemanticLayer;
@@ -123,9 +152,14 @@ export function M4ReplayThreatVisual({
   reviewLabel?: string;
   timelineEndpointRoot?: string;
   evidenceLabel?: string;
+  initialSpatialMode?: LaboratoryMetricSceneMode | null;
+  classifiedSpatialLayer?: M4ReplayClassifiedSpatialLayer;
+  onActiveSequenceChange?: (sequence: number | null) => void;
 }) {
   const [mediaMode, setMediaMode] = useState<M4ThreatMediaMode | null>("video");
-  const [spatialMode, setSpatialMode] = useState<LaboratoryMetricSceneMode | null>(null);
+  const [spatialMode, setSpatialMode] = useState<LaboratoryMetricSceneMode | null>(
+    initialSpatialMode,
+  );
   const [showCurrentIncrement, setShowCurrentIncrement] = useState(true);
   const [showLocalSurface, setShowLocalSurface] = useState(true);
   const [showRollingMap, setShowRollingMap] = useState(true);
@@ -220,6 +254,9 @@ export function M4ReplayThreatVisual({
   }, [resultId]);
   if (timelineFrame.activeFrame) lastFrameRef.current = timelineFrame.activeFrame;
   const frame = timelineFrame.activeFrame ?? lastFrameRef.current;
+  useEffect(() => {
+    onActiveSequenceChange?.(frame?.sequence ?? null);
+  }, [frame?.sequence, onActiveSequenceChange]);
   const lastSpatialFrameRef = useRef<{
     resultId: string;
     frame: M4ThreatTimelineFrame;
@@ -303,12 +340,12 @@ export function M4ReplayThreatVisual({
     );
   }, [frame, metadata.timeline, showStaticObstacles]);
   const activeBoxes = useMemo(
-    () => [
+    () => classifiedSpatialLayer ? [] : [
       ...boxes(frame?.cameraProposals ?? []),
       ...staticObstacleBoxes,
       ...reviewAnchorBoxes,
     ],
-    [frame, reviewAnchorBoxes, staticObstacleBoxes],
+    [classifiedSpatialLayer, frame, reviewAnchorBoxes, staticObstacleBoxes],
   );
   const semanticClasses = useMemo<readonly RecordedEvidenceSemanticClass[]>(
     () => semantic?.taxonomy.map((item) => ({
@@ -363,6 +400,68 @@ export function M4ReplayThreatVisual({
       return status === 2 || status === 3 ? classId : null;
     });
   }, [semantic, semanticIntegrityError, showSpatialSemantic, spatialFrame, spatialSemanticFrame]);
+  const classifiedSpatialFrame = !displayingBufferedFrame
+    && classifiedSpatialLayer?.frame?.sourceSequence === frame?.sequence
+    && spatialFrame?.sequence === frame?.sequence
+    ? classifiedSpatialLayer?.frame ?? null
+    : null;
+  const nominalSensorHeightM = metadata.timeline?.rig.nominalSensorHeightM ?? 0;
+  const mapGravityLocalSensorToBodyGround = useCallback((
+    point: readonly [number, number, number],
+  ): readonly [number, number, number] => {
+    const basis = spatialFrame?.bodyFrame?.basisMapFromBody;
+    const rotated: readonly [number, number, number] = basis ? [
+      basis[0][0] * point[0] + basis[1][0] * point[1] + basis[2][0] * point[2],
+      basis[0][1] * point[0] + basis[1][1] * point[1] + basis[2][1] * point[2],
+      basis[0][2] * point[0] + basis[1][2] * point[1] + basis[2][2] * point[2],
+    ] : point;
+    // TGS evidence is translation-only map-gravity-local with the current LiDAR
+    // as its origin. The metric scene uses the body ground projection as z=0.
+    return [rotated[0], rotated[1], rotated[2] + nominalSensorHeightM];
+  }, [nominalSensorHeightM, spatialFrame?.bodyFrame?.basisMapFromBody]);
+  const classifiedPointsBody = useMemo(
+    () => classifiedSpatialFrame?.pointsMapGravityLocalXyzM.map(
+      mapGravityLocalSensorToBodyGround,
+    ) ?? [],
+    [classifiedSpatialFrame, mapGravityLocalSensorToBodyGround],
+  );
+  const classifiedCellsBody = useMemo<readonly LaboratoryMetricCellEvidence[]>(
+    () => classifiedSpatialFrame?.cellsMapGravityLocal.map((cell) => {
+      const body = mapGravityLocalSensorToBodyGround([
+        cell.centerXyM[0],
+        cell.centerXyM[1],
+        0,
+      ]);
+      const [minimumSensorRelativeZ, maximumSensorRelativeZ] = cell.zBoundsM;
+      return {
+        centerBodyXyM: [body[0], body[1]],
+        zBoundsM: [
+          minimumSensorRelativeZ === null
+            ? null
+            : minimumSensorRelativeZ + nominalSensorHeightM,
+          maximumSensorRelativeZ === null
+            ? null
+            : maximumSensorRelativeZ + nominalSensorHeightM,
+        ],
+        state: cell.state,
+      };
+    }) ?? [],
+    [classifiedSpatialFrame, mapGravityLocalSensorToBodyGround, nominalSensorHeightM],
+  );
+  const classifiedCellCounts = useMemo(() => ({
+    ground: classifiedSpatialFrame?.cellsMapGravityLocal.filter(
+      (cell) => cell.state === "ground-support",
+    ).length ?? 0,
+    occupied: classifiedSpatialFrame?.cellsMapGravityLocal.filter(
+      (cell) => cell.state === "nonground-occupied",
+    ).length ?? 0,
+    rejected: classifiedSpatialFrame?.cellsMapGravityLocal.filter(
+      (cell) => cell.state === "unknown-rejected",
+    ).length ?? 0,
+    unobserved: classifiedSpatialFrame?.cellsMapGravityLocal.filter(
+      (cell) => cell.state === "unobserved",
+    ).length ?? 0,
+  }), [classifiedSpatialFrame]);
   const sceneObstacles = useMemo(() => spatialFrame?.metricObstacles.map((obstacle) => ({
     id: obstacle.componentId,
     decision: obstacle.assessment.decision,
@@ -522,7 +621,32 @@ export function M4ReplayThreatVisual({
     </div>
   ) : null;
 
-  const spatialLayerControls = (
+  const spatialLayerControls = classifiedSpatialLayer ? (
+    <div
+      className="m4-replay-threat-visual__pane-layer-controls"
+      role="group"
+      aria-label={`Слои ${classifiedSpatialLayer.label}`}
+    >
+      <Button
+        size="compact"
+        shape="pill"
+        variant={showCurrentIncrement ? "primary" : "secondary"}
+        aria-pressed={showCurrentIncrement}
+        onClick={() => setShowCurrentIncrement((visible) => !visible)}
+      >
+        {classifiedSpatialLayer.pointLayerLabel}
+      </Button>
+      <Button
+        size="compact"
+        shape="pill"
+        variant={showRollingMap ? "primary" : "secondary"}
+        aria-pressed={showRollingMap}
+        onClick={() => setShowRollingMap((visible) => !visible)}
+      >
+        {classifiedSpatialLayer.cellLayerLabel}
+      </Button>
+    </div>
+  ) : (
     <div
       className="m4-replay-threat-visual__pane-layer-controls"
       role="group"
@@ -626,7 +750,7 @@ export function M4ReplayThreatVisual({
         value={String(selectedReviewAnchorIndex)}
         options={reviewAnchors.map((anchor, index) => ({
           value: String(index),
-          label: `${index + 1}/${reviewAnchors.length} · кадр ${anchor.sourceSequence + 1} · ${anchor.matchedAtThreshold ? "покрыт" : "пропуск"}`,
+          label: `${index + 1}/${reviewAnchors.length} · кадр ${anchor.sourceSequence + 1} · ${anchor.statusLabel ?? (anchor.matchedAtThreshold ? "покрыт" : "пропуск")}`,
         }))}
         variant="split"
         menuWidth="anchor"
@@ -671,39 +795,48 @@ export function M4ReplayThreatVisual({
       </div>
       <div>
         <span>Spatial evidence</span>
-        <strong>
-          {currentIncrementObstacles.length} current · {rollingMapObstacles.length} rolling
-          {metadata.timeline.occupancyProvenanceDelivery
-            ? ` · ${lowStepObstacles.length} low-step`
-            : ""}
-        </strong>
-        <small>
-          {spatialFrame
-            ? `${spatialFrame.pointCloudSampleCount}/${spatialFrame.pointCloudSourceCount} exact · ${localSurface.pointsBodyXyzM.length} local SLAM / ${localSurface.sourceFrameCount} frames`
-            : "квалифицированный spatial frame ещё не получен"}
-          {frame.worldStateAvailable
-            ? " · world-state delivered"
-            : ` · world-state gap (${frame.terminalOutcome})`}
-          {accumulatedCameraPoints
-            ? ` · camera points ${accumulatedCameraPoints.sampleCount}/${accumulatedCameraPoints.projectedPointCount} · causal ${accumulatedCameraPoints.windowSeconds.toFixed(1)} с / ${accumulatedCameraPoints.sourceFrameCount} frames`
-            : pointCloudOverlay
-              ? ` · camera points ${frame.cameraProjectedSampleCount}/${frame.cameraProjectedPointCount} exact-current · накопление загружается`
-              : showMediaPoints && cameraPointOverlay.error
-                ? " · накопленное camera cloud недоступно"
-                : ""}
-          {semantic && spatialSemanticFrame
-            ? ` · semantic L ${spatialSemanticFrame.counts.labeled} · A ${spatialSemanticFrame.counts.ambiguous} · U ${spatialSemanticFrame.counts.unprojected} · Ø ${spatialSemanticFrame.counts.absent}`
-            : semantic ? " · semantic buffer" : ""}
-        </small>
+        <strong>{classifiedSpatialLayer
+          ? classifiedSpatialFrame
+            ? `${classifiedSpatialFrame.pointsMapGravityLocalXyzM.length.toLocaleString("ru-RU")} TGS points · ${classifiedSpatialFrame.cellsMapGravityLocal.length.toLocaleString("ru-RU")} cells`
+            : "TGS spatial buffer"
+          : `${currentIncrementObstacles.length} current · ${rollingMapObstacles.length} rolling${metadata.timeline.occupancyProvenanceDelivery ? ` · ${lowStepObstacles.length} low-step` : ""}`}</strong>
+        <small>{classifiedSpatialLayer
+          ? classifiedSpatialFrame
+            ? "map-gravity-local · all eligible points accounted · causal rolling 1 s"
+            : classifiedSpatialLayer.error ?? `Открываем ${classifiedSpatialLayer.label}`
+          : (
+              <>
+                {spatialFrame
+                  ? `${spatialFrame.pointCloudSampleCount}/${spatialFrame.pointCloudSourceCount} exact · ${localSurface.pointsBodyXyzM.length} local SLAM / ${localSurface.sourceFrameCount} frames`
+                  : "квалифицированный spatial frame ещё не получен"}
+                {frame.worldStateAvailable
+                  ? " · world-state delivered"
+                  : ` · world-state gap (${frame.terminalOutcome})`}
+                {accumulatedCameraPoints
+                  ? ` · camera points ${accumulatedCameraPoints.sampleCount}/${accumulatedCameraPoints.projectedPointCount} · causal ${accumulatedCameraPoints.windowSeconds.toFixed(1)} с / ${accumulatedCameraPoints.sourceFrameCount} frames`
+                  : pointCloudOverlay
+                    ? ` · camera points ${frame.cameraProjectedSampleCount}/${frame.cameraProjectedPointCount} exact-current · накопление загружается`
+                    : showMediaPoints && cameraPointOverlay.error
+                      ? " · накопленное camera cloud недоступно"
+                      : ""}
+                {semantic && spatialSemanticFrame
+                  ? ` · semantic L ${spatialSemanticFrame.counts.labeled} · A ${spatialSemanticFrame.counts.ambiguous} · U ${spatialSemanticFrame.counts.unprojected} · Ø ${spatialSemanticFrame.counts.absent}`
+                  : semantic ? " · semantic buffer" : ""}
+              </>
+            )}</small>
       </div>
       <div>
-        <span>Virtual corridor</span>
-        <strong>
-          {spatialFrame?.decisionCounts.threat ?? 0} threat · nearest {nearest === null ? "—" : `${nearest.toFixed(2)} м`}
-        </strong>
-        <small>
-          {metadata.timeline.corridor.forwardLengthM} м · body {metadata.timeline.rig.lengthM}×{metadata.timeline.rig.widthM} м · REPLAY-SIMULATED
-        </small>
+        <span>{classifiedSpatialLayer ? "TGS fail-closed" : "Virtual corridor"}</span>
+        <strong>{classifiedSpatialLayer
+          ? classifiedSpatialFrame
+            ? `${classifiedCellCounts.occupied} occupied · ${classifiedCellCounts.rejected} rejected · ${classifiedCellCounts.unobserved} unobserved`
+            : classifiedSpatialLayer.loading || displayingBufferedFrame ? "loading" : "unavailable"
+          : `${spatialFrame?.decisionCounts.threat ?? 0} threat · nearest ${nearest === null ? "—" : `${nearest.toFixed(2)} м`}`}</strong>
+        <small>{classifiedSpatialLayer
+          ? classifiedSpatialFrame
+            ? `${classifiedCellCounts.ground} ground-support · visual review only · navigation authority OFF`
+            : "visual review only · navigation authority OFF"
+          : `${metadata.timeline.corridor.forwardLengthM} м · body ${metadata.timeline.rig.lengthM}×${metadata.timeline.rig.widthM} м · REPLAY-SIMULATED`}</small>
       </div>
     </div>
   ) : undefined;
@@ -802,26 +935,50 @@ export function M4ReplayThreatVisual({
             </div>
           </div>
         ) : null}
-        {spatialFrame ? (
+        {spatialFrame && (!classifiedSpatialLayer || classifiedSpatialFrame) ? (
           <LaboratoryMetricEvidenceScene
             ref={metricSceneRef}
-            pointCloudBodyXyzM={spatialFrame.pointCloudBodyXyzM}
-            localSurfaceBodyXyzM={localSurface.pointsBodyXyzM}
-            obstacles={sceneObstacles}
+            pointCloudBodyXyzM={classifiedSpatialFrame
+              ? classifiedPointsBody
+              : spatialFrame.pointCloudBodyXyzM}
+            localSurfaceBodyXyzM={classifiedSpatialFrame ? [] : localSurface.pointsBodyXyzM}
+            obstacles={classifiedSpatialFrame ? [] : sceneObstacles}
             rig={timeline.rig}
             corridor={timeline.corridor}
-            occupiedVoxelSizeM={timeline.occupiedVoxelSizeM}
+            occupiedVoxelSizeM={classifiedSpatialFrame?.cellSizeM ?? timeline.occupiedVoxelSizeM}
             mode={spatialMode}
             label={`${evidenceLabel} exact current increment, bounded local SLAM surface and rolling occupancy`}
             showCurrentIncrement={showCurrentIncrement}
-            showLocalSurface={showLocalSurface}
+            showLocalSurface={classifiedSpatialFrame ? false : showLocalSurface}
             showRollingMap={showRollingMap}
-            showLowStep={showLowStep}
-            pointSemanticClassIds={alignedSemanticPointIds}
-            semanticClasses={semanticClasses}
-            semanticPalette={semanticPalette}
+            showLowStep={classifiedSpatialFrame ? false : showLowStep}
+            pointSemanticClassIds={classifiedSpatialFrame
+              ? classifiedSpatialFrame.pointClassIds
+              : alignedSemanticPointIds}
+            semanticClasses={classifiedSpatialFrame
+              ? classifiedSpatialFrame.classes
+              : semanticClasses}
+            semanticPalette={classifiedSpatialFrame
+              ? classifiedSpatialFrame.palette
+              : semanticPalette}
+            classifiedCells={classifiedCellsBody}
+            classifiedCellSizeM={classifiedSpatialFrame?.cellSizeM}
+            showClassifiedCells={showRollingMap}
           />
         ) : null}
+        {classifiedSpatialLayer && !classifiedSpatialFrame ? (
+          <div className="l3-visual-audit__state" role={classifiedSpatialLayer.error ? "alert" : "status"}>
+            {classifiedSpatialLayer.loading || displayingBufferedFrame
+              ? <span className="busy-indicator" aria-hidden="true" />
+              : <Icon name="alert" size={18} />}
+            <span>{classifiedSpatialLayer.error
+              ?? (classifiedSpatialLayer.loading || displayingBufferedFrame
+                ? `Открываем ${classifiedSpatialLayer.label}`
+                : classifiedSpatialLayer.expectedAtSequence
+                ? `Открываем ${classifiedSpatialLayer.label}`
+                : `${classifiedSpatialLayer.label} рассчитан только на 10 контрольных кадров.`)}</span>
+          </div>
+        ) : null}
         {frame && !frame.spatialAvailable ? (
           <div className="m4-replay-threat-visual__pane-status" role="status">
             {spatialFrame

apps/control-station/src/workspaces/laboratory/laboratoryArchiveProfiles.ts

@@ -105,6 +105,13 @@ const KNOWN_WORKS: Readonly<Record<Exclude<LaboratoryWorkId, `session:${string}`
     experimentName: "RF-DETR native risk review and temporal identity",
     variantName: "M4.8Q · native raw KB4 review · quality not adjudicated",
   },
+  "m49-tgs-fail-closed-evidence": {
+    profileId: "rig-dual-evidence-virtual-corridor-v1",
+    profileName: (rigLabel) => `${rig(rigLabel)} RIGHT · Camera + gravity-aligned LiDAR`,
+    experimentId: "m49-tgs-fail-closed-evidence",
+    experimentName: "TRAVEL TGS fail-closed traversability evidence",
+    variantName: "M4.9T4 · 10 anchors · causal rolling 1 s · AOS OFF",
+  },
   "m47-reference-graph-shadow": {
     profileId: "rig-dual-evidence-virtual-corridor-v1",
     profileName: (rigLabel) => `${rig(rigLabel)} RIGHT · Camera + LiDAR dual evidence`,

apps/control-station/src/workspaces/laboratory/useAdvancedLaboratoryCatalog.ts

@@ -25,6 +25,7 @@ function mergeResults(
     m48r3StaticOccupancy: next.m48r3StaticOccupancy ?? current.m48r3StaticOccupancy,
     m48s: next.m48s ?? current.m48s,
     m48t: next.m48t ?? current.m48t,
+    m49Tgs: next.m49Tgs ?? current.m49Tgs,
     m4Threat: next.m4Threat ?? current.m4Threat,
     l3: next.l3 ?? current.l3,
     l31: next.l31 ?? current.l31,
@@ -124,6 +125,7 @@ export function useAdvancedLaboratoryCatalog({
         "m48-small-static-passage-regression",
         "m48-static-occupancy-qualification",
         "m48r3-static-occupancy-shadow",
+        "m49-tgs-fail-closed-evidence",
       ].includes(selectedWorkId)
       && !indexedResultId
     ) return;