Open3d学习计划——高级篇 3（点云全局配准）

ICP配准和彩色点云配准都被称为局部点云配准方法，因为他们都依赖一个粗糙的对齐作为初始化。本篇教程将会展现另一种被称为全局配准的配准方法.这种系列的算法不要求一个初始化的对齐,通常会输出一个没那么精准的对齐结果,并且使用该结果作为局部配准的初始化.

可视化

该辅助函数可以将配准的源点云和目标点云一起可视化.

def draw_registration_result(source, target, transformation):
    source_temp = copy.deepcopy(source)
    target_temp = copy.deepcopy(target)
    source_temp.paint_uniform_color([1, 0.706, 0])
    target_temp.paint_uniform_color([0, 0.651, 0.929])
    source_temp.transform(transformation)
    o3d.visualization.draw_geometries([source_temp, target_temp])

注意:这里原来的教程里可视化函数都加了初始视角之类的,但是很多人反映这个会报错,并且官方函数里也没给出可接受的参数,所以在这里把初始视角的参数都去掉了

提取几何特征

我们降采样点云,估计法线,之后对每个点计算FPFH特征.FPFH特征是一个描述点的局部几何属性的33维的向量.在33维空间中进行最近邻查询可以返回具有近似几何结构的点.详细细节请查看 [Rasu2009].

def preprocess_point_cloud(pcd, voxel_size):
    print(":: Downsample with a voxel size %.3f." % voxel_size)
    pcd_down = pcd.voxel_down_sample(voxel_size)

    radius_normal = voxel_size * 2
    print(":: Estimate normal with search radius %.3f." % radius_normal)
    pcd_down.estimate_normals(
        o3d.geometry.KDTreeSearchParamHybrid(radius=radius_normal, max_nn=30))

    radius_feature = voxel_size * 5
    print(":: Compute FPFH feature with search radius %.3f." % radius_feature)
    pcd_fpfh = o3d.registration.compute_fpfh_feature(
        pcd_down,
        o3d.geometry.KDTreeSearchParamHybrid(radius=radius_feature, max_nn=100))
    return pcd_down, pcd_fpfh

输入

以下代码从两个文件中读取源点云和目标点云.这一对点云使用单位矩阵作为初始矩阵之后是不对齐的.

def prepare_dataset(voxel_size):
    print(":: Load two point clouds and disturb initial pose.")
    source = o3d.io.read_point_cloud("../../TestData/ICP/cloud_bin_0.pcd")
    target = o3d.io.read_point_cloud("../../TestData/ICP/cloud_bin_1.pcd")
    trans_init = np.asarray([[0.0, 0.0, 1.0, 0.0], [1.0, 0.0, 0.0, 0.0],
                             [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0]])
    source.transform(trans_init)
    draw_registration_result(source, target, np.identity(4))

    source_down, source_fpfh = preprocess_point_cloud(source, voxel_size)
    target_down, target_fpfh = preprocess_point_cloud(target, voxel_size)
    return source, target, source_down, target_down, source_fpfh, target_fpfh

voxel_size = 0.05 # means 5cm for this dataset
source, target, source_down, target_down, source_fpfh, target_fpfh = prepare_dataset(voxel_size)

:: Load two point clouds and disturb initial pose.
:: Downsample with a voxel size 0.050.
:: Estimate normal with search radius 0.100.
:: Compute FPFH feature with search radius 0.250.
:: Downsample with a voxel size 0.050.
:: Estimate normal with search radius 0.100.
:: Compute FPFH feature with search radius 0.250.

RANSAC

我们使用RANSAC进行全局配准.在RANSAC迭代中，我们每次从源点云中选取　ransac_n 个随机点.通过在33维FPFH特征空间中查询最邻近,可以在目标点云中找到他们的对应点.剪枝步骤需要使用快速修剪算法来提早拒绝错误匹配.
Open3d提供以下剪枝算法:

• CorrespondenceCheckerBasedOnDistance检查对应的点云是否接近(也就是距离是否小于指定阈值)
• CorrespondenceCheckerBasedOnEdgeLength检查从源点云和目标点云对应中分别画上两条任意边(两个顶点连成的线)是否近似.本教程检验的是 ∣ ∣ e d g e s o u r c e ∣ ∣ > 0.9 ∗ ∣ ∣ e d g e t a r g e t ∣ ∣ ||edge_{source}|| > 0.9 * ||edge_{target}|| ∣∣edgesource​∣∣>0.9∗∣∣edgetarget​∣∣和 0.9 ∗ ∣ ∣ e d g e s o u r c e ∣ ∣ < ∣ ∣ e d g e t a r g e t ∣ ∣ 0.9*||edge_{source}|| < ||edge_{target}|| 0.9∗∣∣edgesource​∣∣<∣∣edgetarget​∣∣ 是真.
• CorrespondenceCheckerBasedOnNormal考虑的所有的对应的顶点法线的密切关系.他计算了两个法线向量的点积.使用弧度作为阈值.

只有通过剪枝步骤的匹配才用于转换,该转换将在整个点云上进行验证.
核心函数是 registration_ransac_based_on_feature_matching. RANSACConvergenceCriteria是里面一个十分重要的超参数.他定义了RANSAC迭代的最大次数和验证的最大次数.这两个值越大,那么结果越准确,但同时也要花费更多的时间.
我们是基于[Choi2015]提供的的经验来设置RANSAC的超参数.

def execute_global_registration(source_down, target_down, source_fpfh,
                               target_fpfh, voxel_size):
   distance_threshold = voxel_size * 1.5
   print(":: RANSAC registration on downsampled point clouds.")
   print("   Since the downsampling voxel size is %.3f," % voxel_size)
   print("   we use a liberal distance threshold %.3f." % distance_threshold)
   result = o3d.registration.registration_ransac_based_on_feature_matching(
       source_down, target_down, source_fpfh, target_fpfh, distance_threshold,
       o3d.registration.TransformationEstimationPointToPoint(False), 4, [
           o3d.registration.CorrespondenceCheckerBasedOnEdgeLength(0.9),
           o3d.registration.CorrespondenceCheckerBasedOnDistance(
               distance_threshold)
       ], o3d.registration.RANSACConvergenceCriteria(4000000, 500))
   return result

result_ransac = execute_global_registration(source_down, target_down,
                                           source_fpfh, target_fpfh,
                                           voxel_size)
print(result_ransac)
draw_registration_result(source_down, target_down, result_ransac.transformation)

:: RANSAC registration on downsampled point clouds.
Since the downsampling voxel size is 0.050,
we use a liberal distance threshold 0.075.
registration::RegistrationResult with fitness=6.773109e-01, inlier_rmse=3.332039e-02, and correspondence_set size of 3224
Access transformation to get result.

局部优化

由于性能原因,全局配准只能在大规模降采样的点云上执行,配准的结果不够精细,我们使用 Point-to-plane ICP 去进一步优化配准结果.

def refine_registration(source, target, source_fpfh, target_fpfh, voxel_size):
    distance_threshold = voxel_size * 0.4
    print(":: Point-to-plane ICP registration is applied on original point")
    print("   clouds to refine the alignment. This time we use a strict")
    print("   distance threshold %.3f." % distance_threshold)
    result = o3d.registration.registration_icp(
        source, target, distance_threshold, result_ransac.transformation,
        o3d.registration.TransformationEstimationPointToPlane())
    return result

result_icp = refine_registration(source, target, source_fpfh, target_fpfh,
                                 voxel_size)
print(result_icp)
draw_registration_result(source, target, result_icp.transformation)

:: Point-to-plane ICP registration is applied on original point
clouds to refine the alignment. This time we use a strict
distance threshold 0.020.
registration::RegistrationResult with fitness=6.210275e-01, inlier_rmse=6.565175e-03, and correspondence_set size of 123482
Access transformation to get result.

快速全局配准

由于无数的模型推荐和评估,导致基于RANSAC的全局配准需要很长的时间.
[Zhou2016] 提出了一种加速的方法,该方法可以快速的优化几乎没有对应关系的线处理权重( [Zhou2016] introduced a faster approach that quickly optimizes line process weights of few correspondences).这样在每次迭代的时候没有模型建议和评估,该方法就在计算的时候节约的大量的时间.(建议看看原论文,这个感觉翻译不好,有更好建议的欢迎留言.)
这篇教程比较了基于RANSAC的全局配准和[Zhou2016]方法的运行时间.

输入

我们使用上面全局配准的输入例子.

voxel_size = 0.05  # means 5cm for the dataset
source, target, source_down, target_down, source_fpfh, target_fpfh = \
        prepare_dataset(voxel_size)

:: Load two point clouds and disturb initial pose.
:: Downsample with a voxel size 0.050.
:: Estimate normal with search radius 0.100.
:: Compute FPFH feature with search radius 0.250.
:: Downsample with a voxel size 0.050.
:: Estimate normal with search radius 0.100.
:: Compute FPFH feature with search radius 0.250.

基准

在下面代码中,我们将计时全局配准算法.

start = time.time()
result_ransac = execute_global_registration(source_down, target_down,
                                            source_fpfh, target_fpfh,
                                            voxel_size)
print("Global registration took %.3f sec.\n" % (time.time() - start))
print(result_ransac)
draw_registration_result(source_down, target_down,
                         result_ransac.transformation)

:: RANSAC registration on downsampled point clouds.
Since the downsampling voxel size is 0.050,
we use a liberal distance threshold 0.075.
Global registration took 0.085 sec.
registration::RegistrationResult with fitness=6.760504e-01, inlier_rmse=2.596653e-02, and correspondence_set size of 3218
Access transformation to get result.

快速全局配准

我们采用和基准相同的输入,下面的代码调用了了[Zhou2016]的实现.

def execute_fast_global_registration(source_down, target_down, source_fpfh,
                                     target_fpfh, voxel_size):
    distance_threshold = voxel_size * 0.5
    print(":: Apply fast global registration with distance threshold %.3f" \
            % distance_threshold)
    result = o3d.registration.registration_fast_based_on_feature_matching(
        source_down, target_down, source_fpfh, target_fpfh,
        o3d.registration.FastGlobalRegistrationOption(
            maximum_correspondence_distance=distance_threshold))
    return result

start = time.time()
result_fast = execute_fast_global_registration(source_down, target_down,
                                               source_fpfh, target_fpfh,
                                               voxel_size)
print("Fast global registration took %.3f sec.\n" % (time.time() - start))
print(result_fast)
draw_registration_result(source_down, target_down,
                         result_fast.transformation)

:: Apply fast global registration with distance threshold 0.025
Fast global registration took 0.128 sec.
registration::RegistrationResult with fitness=5.054622e-01, inlier_rmse=1.743545e-02, and correspondence_set size of 2406
Access transformation to get result.

经过适当的配置,快速全局配准的精度甚至可以和ICP相媲美.更多实验结果请参阅[Zhou2016].

关于翻译大家有更好的意见欢迎评论一起学习！！！

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