feat: 算法升级、整车配准集成与性能分析文档

算法更新: delivery v1.2.1、平整度v1.5、测距v2.2、整车配准v1.3
适配器: vehicle-registration 新增、各算法 manifest 和 adapter 更新
后端: taskCadFiles/source 字段迁移、derivedPointClouds 注册、取消任务联动
前端: ElapsedTime 时长显示、两步算法流程、canRun 逻辑修复
文档: 新增5份算法性能分析报告

alg/平整度、平面度、轮廓测量v1.5/registration.py

@@ -0,0 +1,110 @@
+from __future__ import annotations
+
+import copy
+from dataclasses import dataclass
+
+import numpy as np
+import open3d as o3d
+from scipy.spatial.transform import Rotation
+
+
+@dataclass
+class RegistrationResult:
+    transformation: np.ndarray
+    fitness: float
+    rmse: float
+    target: o3d.geometry.PointCloud
+    recovered: o3d.geometry.PointCloud
+
+
+class PointCloudRegistration:
+    """RANSAC + ICP point-cloud registration."""
+
+    def rotation_matrix(self, roll: float = 10.0, pitch: float = 10.0, yaw: float = 10.0) -> np.ndarray:
+        return Rotation.from_euler("xyz", [roll, pitch, yaw], degrees=True).as_matrix()
+
+    def translation_vector(self, trans_range: float = 0.0, seed: int | None = None) -> np.ndarray:
+        rng = np.random.default_rng(seed)
+        return rng.uniform(-trans_range, trans_range, 3)
+
+    def transform_point_cloud(
+        self,
+        pointcloud: o3d.geometry.PointCloud,
+        rotation: np.ndarray,
+        translation: np.ndarray,
+    ) -> o3d.geometry.PointCloud:
+        transformed = copy.deepcopy(pointcloud)
+        points = np.asarray(transformed.points)
+        transformed.points = o3d.utility.Vector3dVector((rotation @ points.T).T + translation)
+        return transformed
+
+    def register(
+        self,
+        source: o3d.geometry.PointCloud,
+        target: o3d.geometry.PointCloud,
+        voxel_size: float = 10.0,
+    ) -> tuple[np.ndarray, float, float]:
+        source_down = source.voxel_down_sample(voxel_size)
+        target_down = target.voxel_down_sample(voxel_size)
+
+        normal_radius = voxel_size * 2
+        source_down.estimate_normals(o3d.geometry.KDTreeSearchParamHybrid(radius=normal_radius, max_nn=30))
+        target_down.estimate_normals(o3d.geometry.KDTreeSearchParamHybrid(radius=normal_radius, max_nn=30))
+
+        feature_radius = voxel_size * 5
+        source_fpfh = o3d.pipelines.registration.compute_fpfh_feature(
+            source_down,
+            o3d.geometry.KDTreeSearchParamHybrid(radius=feature_radius, max_nn=100),
+        )
+        target_fpfh = o3d.pipelines.registration.compute_fpfh_feature(
+            target_down,
+            o3d.geometry.KDTreeSearchParamHybrid(radius=feature_radius, max_nn=100),
+        )
+
+        distance_threshold = voxel_size * 1.5
+        result_ransac = o3d.pipelines.registration.registration_ransac_based_on_feature_matching(
+            source_down,
+            target_down,
+            source_fpfh,
+            target_fpfh,
+            mutual_filter=True,
+            max_correspondence_distance=distance_threshold,
+            estimation_method=o3d.pipelines.registration.TransformationEstimationPointToPoint(),
+            ransac_n=3,
+            checkers=[
+                o3d.pipelines.registration.CorrespondenceCheckerBasedOnEdgeLength(0.9),
+                o3d.pipelines.registration.CorrespondenceCheckerBasedOnDistance(distance_threshold),
+            ],
+            criteria=o3d.pipelines.registration.RANSACConvergenceCriteria(100000, 0.999),
+        )
+
+        source.estimate_normals(o3d.geometry.KDTreeSearchParamHybrid(radius=0.1, max_nn=30))
+        target.estimate_normals(o3d.geometry.KDTreeSearchParamHybrid(radius=0.1, max_nn=30))
+
+        result_icp = o3d.pipelines.registration.registration_icp(
+            source,
+            target,
+            distance_threshold / 2,
+            result_ransac.transformation,
+            o3d.pipelines.registration.TransformationEstimationPointToPlane(),
+            o3d.pipelines.registration.ICPConvergenceCriteria(max_iteration=200),
+        )
+        return result_icp.transformation, float(result_icp.fitness), float(result_icp.inlier_rmse)
+
+    def run_simulated_registration(
+        self,
+        source: o3d.geometry.PointCloud,
+        voxel_size: float = 10.0,
+        roll: float = 10.0,
+        pitch: float = 10.0,
+        yaw: float = 10.0,
+        trans_range: float = 0.0,
+        random_seed: int | None = None,
+    ) -> RegistrationResult:
+        rotation = self.rotation_matrix(roll, pitch, yaw)
+        translation = self.translation_vector(trans_range, random_seed)
+        target = self.transform_point_cloud(source, rotation, translation)
+        transformation, fitness, rmse = self.register(source, target, voxel_size)
+        recovered = copy.deepcopy(target)
+        recovered.transform(np.linalg.inv(transformation))
+        return RegistrationResult(transformation, fitness, rmse, target, recovered)