1樓：
雷射雷達：
Blueprint:sensor.lidar.ray_cast
Output:carla.LidarMeasurement per step (unless sensor_tick says otherwise).
This sensor simulates a rotating Lidar implemented using ray-casting. The points are computed by adding a laser for each channel distributed in the vertical FOV. The rotation is simulated computing the horizontal angle that the Lidar rotated in a frame.
The point cloud is calculated by doing a ray-cast for each laser in every step: points_per_channel_each_step = points_per_second / (FPS * channels)
A Lidar measurement contains a packet with all the points generated during a 1/FPS interval. During this interval the physics are not updated so all the points in a measurement reflect the same "static picture" of the scene.
公釐波雷達：
Blueprint:sensor.other.radar
Output:carla.RadarMeasurement per step (unless sensor_tick says otherwise).
The sensor creates a conic view that is translated to a 2D point map of the elements in sight and their speed regarding the sensor. This can be used to shape elements and evaluate their movement and direction. Due to the use of polar coordinates, the points will concentrate around the center of the view.
Points measured are contained in carla.RadarMeasurement as an array of carla.RadarDetection, which specifies their polar coordinates, distance and velocity.
This raw data provided by the radar sensor can be easily converted to a format manageable bynumpy:
# To get a numpy [[vel, altitude, azimuth, depthpoints = np.frombuffer(radar_data.raw_data, dtype=np.
dtype('f4'))
points = np.reshape(points, (len(radar_data), 4))
The provided script manual_control.py uses this sensor to show the points being detected and paint them white when static, red when moving towards the object and blue when moving away:
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