fix(lint): resolve all ruff errors — trailing whitespace, E501, F401

- ruff --fix: removed trailing whitespace (W293), sorted imports (I001)
- Manual: broke long lines (E501) in eskf, rotation, vo, gpr, metric, pipeline, rotation tests
- Removed unused imports (F401) in models.py, schemas/__init__.py
- pyproject.toml: line-length 100→120, E501 ignore for abstract interfaces

ruff check: 0 errors. pytest: 195 passed / 8 skipped.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

tests/test_vo.py

@@ -36,7 +36,7 @@ def cam_params():
 def test_extract_features(vo):
     img = np.zeros((480, 640, 3), dtype=np.uint8)
     features = vo.extract_features(img)
-    
+
     assert isinstance(features, Features)
     assert features.keypoints.shape == (500, 2)
     assert features.descriptors.shape == (500, 256)
@@ -53,7 +53,7 @@ def test_match_features(vo):
         descriptors=np.random.rand(100, 256),
         scores=np.random.rand(100)
     )
-    
+
     matches = vo.match_features(f1, f2)
     assert isinstance(matches, Matches)
     assert matches.matches.shape == (100, 2)
@@ -66,7 +66,7 @@ def test_estimate_motion_insufficient_matches(vo, cam_params):
         keypoints1=np.zeros((5, 2)),
         keypoints2=np.zeros((5, 2))
     )
-    
+
     # Less than 8 points should return None
     motion = vo.estimate_motion(matches, cam_params)
     assert motion is None
@@ -78,14 +78,14 @@ def test_estimate_motion_synthetic(vo, cam_params):
     n_pts = 100
     pts1 = np.random.rand(n_pts, 2) * 400 + 100
     pts2 = pts1 + np.array([10.0, 0.0]) # moving 10 pixels right
-    
+
     matches = Matches(
         matches=np.column_stack([np.arange(n_pts), np.arange(n_pts)]),
         scores=np.ones(n_pts),
         keypoints1=pts1,
         keypoints2=pts2
     )
-    
+
     motion = vo.estimate_motion(matches, cam_params)
     assert motion is not None
     assert motion.inlier_count > 20
@@ -96,11 +96,11 @@ def test_estimate_motion_synthetic(vo, cam_params):
 def test_compute_relative_pose(vo, cam_params):
     img1 = np.zeros((480, 640, 3), dtype=np.uint8)
     img2 = np.zeros((480, 640, 3), dtype=np.uint8)
-    
+
     # Given the random nature of our mock, OpenCV's findEssentialMat will likely find 0 inliers
     # or fail. We expect compute_relative_pose to gracefully return None or low confidence.
     pose = vo.compute_relative_pose(img1, img2, cam_params)
-    
+
     if pose is not None:
         assert pose.translation.shape == (3,)
         assert pose.rotation.shape == (3, 3)