52 lines
2.3 KiB
Python
52 lines
2.3 KiB
Python
# 实飞数据训练：将全局地图裁剪并保存
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# 1、注意数据收集时，地面尽量平，且需要为z=0
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# 2、收集数据不平时，修改yaw_angle_radians, pitch_angle_radians平移，并与data collection一致
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# 3、bug：需要打开保存的文件，手动把前面几行的double改成float...
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import open3d as o3d
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import numpy as np
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# 1. 加载点云数据
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point_cloud = o3d.io.read_point_cloud("1.pcd") # 替换为点云文件的路径
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# # 统计离群点移除滤波
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# cl, ind = cropped_point_cloud.remove_statistical_outlier(nb_neighbors=5, std_ratio=1.0) # 调整参数以控制移除离群点的程度
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# filtered_cloud = cropped_point_cloud.select_by_index(ind)
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# 2. 定义旋转角度（偏航角和俯仰角）
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yaw_angle_degrees = -15 # 偏航角（以度为单位）
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pitch_angle_degrees = -3 # 俯仰角（以度为单位）
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# 3. 将角度转换为弧度
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yaw_angle_radians = np.radians(yaw_angle_degrees)
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pitch_angle_radians = np.radians(pitch_angle_degrees)
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yaw_rotation = np.array([[np.cos(yaw_angle_radians), -np.sin(yaw_angle_radians), 0],
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[np.sin(yaw_angle_radians), np.cos(yaw_angle_radians), 0],
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[0, 0, 1]])
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pitch_rotation = np.array([[np.cos(pitch_angle_radians), 0, np.sin(pitch_angle_radians)],
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[0, 1, 0],
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[-np.sin(pitch_angle_radians), 0, np.cos(pitch_angle_radians)]])
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# 4. 平移2米到Z方向
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translation_no = np.array([0, 0, 2]) # 平移2米到Z方向
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# 5. 组合旋转矩阵 R old->new
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R_on = np.dot(yaw_rotation, pitch_rotation) # 内旋是右乘，先yaw后pitch
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# P_n = (R_no * P_o.T).T + t_no = P_o * R_on + t_no
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point_cloud.points = o3d.utility.Vector3dVector(np.dot(np.asarray(point_cloud.points), R_on) + translation_no)
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# o3d.visualization.draw_geometries([point_cloud])
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# 2. 定义裁剪范围
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# 例如，裁剪一个立方体范围，这里给出立方体的最小点和最大点坐标
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min_bound = np.array([-5.0, -18.0, 0]) # 最小点坐标
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max_bound = np.array([150.0, 25.0, 6]) # 最大点坐标
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# 3. 使用crop函数裁剪点云
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cropped_point_cloud = point_cloud.crop(o3d.geometry.AxisAlignedBoundingBox(min_bound, max_bound))
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o3d.io.write_point_cloud("realworld.ply", cropped_point_cloud, write_ascii=True)
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o3d.visualization.draw_geometries([cropped_point_cloud]) |