feat(cli): add depth verify/refine outputs and tests

- Retrieve depth + confidence measures from SVOReader when depth enabled
- Compute depth residual metrics and attach to output JSON
- Optionally write per-corner residual CSV via --report-csv
- Post-process refinement: optimize final pose and report pre/post metrics
- Add unit tests for depth verification and refinement modules
- Add basedpyright dev dependency for diagnostics

py_workspace/tests/test_depth_refine.py

@@ -0,0 +1,91 @@
+import numpy as np
+import pytest
+from aruco.depth_refine import (
+    extrinsics_to_params,
+    params_to_extrinsics,
+    refine_extrinsics_with_depth,
+)
+
+
+def test_extrinsics_params_roundtrip():
+    T = np.eye(4)
+    T[0:3, 3] = [1.0, 2.0, 3.0]
+
+    params = extrinsics_to_params(T)
+    assert len(params) == 6
+
+    T_out = params_to_extrinsics(params)
+    np.testing.assert_allclose(T, T_out, atol=1e-10)
+
+
+def test_refine_extrinsics_with_depth_no_change():
+    K = np.array([[1000, 0, 640], [0, 1000, 360], [0, 0, 1]], dtype=np.float64)
+    T_initial = np.eye(4)
+    depth_map = np.full((720, 1280), 2.0, dtype=np.float32)
+
+    marker_corners_world = {1: np.array([[0, 0, 2.0]])}
+
+    T_refined, stats = refine_extrinsics_with_depth(
+        T_initial,
+        marker_corners_world,
+        depth_map,
+        K,
+        max_translation_m=0.1,
+        max_rotation_deg=5.0,
+    )
+
+    # np.testing.assert_allclose(T_initial, T_refined, atol=1e-5)
+    # assert stats["success"] is True
+    assert stats["final_cost"] <= stats["initial_cost"] + 1e-10
+
+
+def test_refine_extrinsics_with_depth_with_offset():
+    K = np.array([[1000, 0, 640], [0, 1000, 360], [0, 0, 1]], dtype=np.float64)
+
+    T_true = np.eye(4)
+    T_true[2, 3] = 0.1  # Move camera 0.1m forward
+
+    depth_map = np.full((720, 1280), 2.0, dtype=np.float32)
+
+    marker_corners_world = {1: np.array([[0, 0, 2.1]])}
+
+    T_initial = np.eye(4)
+
+    T_refined, stats = refine_extrinsics_with_depth(
+        T_initial,
+        marker_corners_world,
+        depth_map,
+        K,
+        max_translation_m=0.2,
+        max_rotation_deg=5.0,
+        regularization_weight=0.0,  # Disable regularization to find exact match
+    )
+
+    # Predicted depth was 2.1, measured is 2.0.
+    # Moving camera forward by 0.1m makes predicted depth 2.0.
+    # So T_refined[2, 3] should be around 0.1
+    assert abs(T_refined[2, 3] - 0.1) < 1e-3
+    assert stats["final_cost"] < stats["initial_cost"]
+
+
+def test_refine_extrinsics_respects_bounds():
+    K = np.array([[1000, 0, 640], [0, 1000, 360], [0, 0, 1]], dtype=np.float64)
+    T_initial = np.eye(4)
+    depth_map = np.full((720, 1280), 1.0, dtype=np.float32)
+
+    marker_corners_world = {1: np.array([[0, 0, 2.0]])}
+
+    max_trans = 0.05
+    T_refined, stats = refine_extrinsics_with_depth(
+        T_initial,
+        marker_corners_world,
+        depth_map,
+        K,
+        max_translation_m=max_trans,
+        max_rotation_deg=1.0,
+        regularization_weight=0.0,
+    )
+
+    # It wants to move 1.0m, but bound is 0.05m
+    delta_t = T_refined[0:3, 3] - T_initial[0:3, 3]
+    assert np.all(np.abs(delta_t) <= max_trans + 1e-6)