点云数据生成鸟瞰图表示
点云数据
点云数据是点的集合，由激光雷达采集获得，点云数据应表示为具有N行，具有4列的numpy数组。每行对应一个点，该点在空间（x，y，z）使用3个值表示。 第四个值是附加值，通常为反射率(强度)。
鸟瞰图表示
鸟瞰图是俯视视角下点云的一种图形表示。 在自动驾驶中，该表示方法的合理性基于一个前提: 所有的车辆都在地面行驶，因此可以直接将数据展平在x, y平面中。 点云数据转换为鸟瞰图的步骤为：
- 设置感兴趣的区域(Region of Interest), 区域大小为L * W * H.
- 设置分辨率，将Region of Interest栅格化，并将点云的位置映射到鸟瞰图上的像素位置，计算occupancy map
- 按照高度将鸟瞰图划分为不同的通道，以保存点云数据中的高度信息。
鸟瞰图生成代码
import numpy as np
# ==============================================================================
# SCALE_TO_255
# ==============================================================================
def scale_to_255(a, min, max, dtype=np.uint8):
""" Scales an array of values from specified min, max range to 0-255
Optionally specify the data type of the output (default is uint8)
"""
return (((a - min) / float(max - min)) * 255).astype(dtype)
# ==============================================================================
# POINT_CLOUD_2_BIRDSEYE
# ==============================================================================
def point_cloud_2_birdseye(points,
res=0.1,
side_range=(-10., 10.), # left-most to right-most
fwd_range = (-10., 10.), # back-most to forward-most
height_range=(-2., 2.), # bottom-most to upper-most
):
""" Creates an 2D birds eye view representation of the point cloud data.
Args:
points: (numpy array)
N rows of points data
Each point should be specified by at least 3 elements x,y,z
res: (float)
Desired resolution in metres to use. Each output pixel will
represent an square region res x res in size.
side_range: (tuple of two floats)
(-left, right) in metres
left and right limits of rectangle to look at.
fwd_range: (tuple of two floats)
(-behind, front) in metres
back and front limits of rectangle to look at.
height_range: (tuple of two floats)
(min, max) heights (in metres) relative to the origin.
All height values will be clipped to this min and max value,
such that anything below min will be truncated to min, and
the same for values above max.
Returns:
2D numpy array representing an image of the birds eye view.
"""
# EXTRACT THE POINTS FOR EACH AXIS
x_points = points[:, 0]
y_points = points[:, 1]
z_points = points[:, 2]
# FILTER - To return only indices of points within desired cube
# Three filters for: Front-to-back, side-to-side, and height ranges
# Note left side is positive y axis in LIDAR coordinates
f_filt = np.logical_and((x_points > fwd_range[0]), (x_points < fwd_range[1]))
s_filt = np.logical_and((y_points > -side_range[1]), (y_points < -side_range[0]))
filter = np.logical_and(f_filt, s_filt)
indices = np.argwhere(filter).flatten()
# KEEPERS
x_points = x_points[indices]
y_points = y_points[indices]
z_points = z_points[indices]
# CONVERT TO PIXEL POSITION VALUES - Based on resolution
x_img = (-y_points / res).astype(np.int32) # x axis is -y in LIDAR
y_img = (-x_points / res).astype(np.int32) # y axis is -x in LIDAR
# SHIFT PIXELS TO HAVE MINIMUM BE (0,0)
# floor & ceil used to prevent anything being rounded to below 0 after shift
x_img -= int(np.floor(side_range[0] / res))
y_img += int(np.ceil(fwd_range[1] / res))
# CLIP HEIGHT VALUES - to between min and max heights
pixel_values = np.clip(a=z_points,
a_min=height_range[0],
a_max=height_range[1])
# RESCALE THE HEIGHT VALUES - to be between the range 0-255
pixel_values = scale_to_255(pixel_values,
min=height_range[0],
max=height_range[1])
# INITIALIZE EMPTY ARRAY - of the dimensions we want
x_max = 1 + int((side_range[1] - side_range[0]) / res)
y_max = 1 + int((fwd_range[1] - fwd_range[0]) / res)
im = np.zeros([y_max, x_max], dtype=np.uint8)
# FILL PIXEL VALUES IN IMAGE ARRAY
im[y_img, x_img] = pixel_values
return im
pointcloud = np.fromfile(str("000000.bin"), dtype=np.float32, count=-1).reshape([-1, 4])
bev = point_cloud_2_birdseye(pointcloud)
生成的鸟瞰图：
