feat: stage7 — Model Manager (F16) and Sequential VO (F07)

src/gps_denied/schemas/model.py

@@ -0,0 +1,21 @@
+"""Model Manager schemas (Component F16)."""
+
+from typing import Any
+from pydantic import BaseModel
+
+class ModelConfig(BaseModel):
+    """Configuration for an ML model."""
+    model_name: str
+    model_path: str
+    format: str
+    precision: str  # "fp16", "fp32"
+    warmup_iterations: int = 3
+
+class InferenceEngine:
+    """Base definition for an inference engine."""
+    def __init__(self, model_name: str, format_: str):
+        self.model_name = model_name
+        self.format = format_
+
+    def infer(self, input_data: Any) -> Any:
+        raise NotImplementedError

src/gps_denied/schemas/vo.py

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+"""Sequential Visual Odometry schemas (Component F07)."""
+
+from typing import Optional
+
+import numpy as np
+from pydantic import BaseModel
+
+
+class Features(BaseModel):
+    """Extracted image features (e.g., from SuperPoint)."""
+    model_config = {"arbitrary_types_allowed": True}
+    
+    keypoints: np.ndarray  # (N, 2)
+    descriptors: np.ndarray  # (N, 256)
+    scores: np.ndarray  # (N,)
+
+
+class Matches(BaseModel):
+    """Matches between two sets of features (e.g., from LightGlue)."""
+    model_config = {"arbitrary_types_allowed": True}
+    
+    matches: np.ndarray  # (M, 2)
+    scores: np.ndarray  # (M,)
+    keypoints1: np.ndarray  # (M, 2)
+    keypoints2: np.ndarray  # (M, 2)
+
+
+class RelativePose(BaseModel):
+    """Relative pose between two frames."""
+    model_config = {"arbitrary_types_allowed": True}
+    
+    translation: np.ndarray  # (3,)
+    rotation: np.ndarray  # (3, 3)
+    confidence: float
+    inlier_count: int
+    total_matches: int
+    tracking_good: bool
+    scale_ambiguous: bool = True
+    chunk_id: Optional[str] = None
+
+
+class Motion(BaseModel):
+    """Motion estimate from OpenCV."""
+    model_config = {"arbitrary_types_allowed": True}
+    
+    translation: np.ndarray  # (3,) unit vector
+    rotation: np.ndarray  # (3, 3) rotation matrix
+    inliers: np.ndarray  # Boolean mask of inliers
+    inlier_count: int