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Vorwort

Das Folgende zeigt eine LiDAR-Sensor-Demo, die auf Ubuntu 18.4 ausgeführt werden kann. Die spezifischen Effekte, die erzielt werden können, sind:

1. Kontinuierliche Echtzeitanzeige des LiDAR-Sensorbildbildschirms

2. Kann an der vorgesehenen Position am Auto montiert werden

3. Definieren Sie den entsprechenden Feldbetrachtungswinkel, der Betrachtungswinkelbereich beträgt 0 bis 360 Grad

4. Die gesammelten Daten werden im npy-Dateiformat gespeichert

1. Beschreibung des Demomoduls

1. Definieren Sie die entsprechenden Daten

Spezifische Anweisungen finden Sie in der offiziellen Website-Beschreibung. Dieser Artikel gibt die offizielle Website-Adresse an: [Favoriten] Integration hochwertiger Lernmaterialien in das gesamte Netzwerk des CARLA-Simulators [Getting Started-1]

 #-------------------------- 进入传感器部分 --------------------------#
        sensor_queue = Queue()
        lidar_bp = blueprint_library.find('sensor.lidar.ray_cast')

        lidar_bp.set_attribute('channels', '64')
        lidar_bp.set_attribute('points_per_second', '200000')
        lidar_bp.set_attribute('range', '64')
        lidar_bp.set_attribute('rotation_frequency','20') 
        lidar_bp.set_attribute('horizontal_fov', '360')

2. Definition der Position des Radars am Fahrzeug:

        lidar01 = world.spawn_actor(lidar_bp, carla.Transform(carla.Location(z=args.sensor_h)), attach_to=ego_vehicle)
        lidar01.listen(lambda data: sensor_callback(data, sensor_queue, "lidar"))
        sensor_list.append(lidar01)

        
        #-------------------------- 传感器设置完毕 --------------------------#

3. Visuelle Anzeige der Kamera:

w_frame = world.get_snapshot().frame
            print("\nWorld's frame: %d" % w_frame)
            try:
                lidars = []
                for i in range (0, len(sensor_list)):
                    s_frame, s_name, s_data = sensor_queue.get(True, 1.0)
                    print("    Frame: %d   Sensor: %s" % (s_frame, s_name))
                    sensor_type = s_name.split('_')[0]
                    if sensor_type == 'lidar':
                        lidar = _parse_lidar_cb(s_data)

                cv2.imshow('vizs', visualize_data(lidar))
                cv2.waitKey(100)

4. Daten speichern:

mkdir_folder(args.save_path)
filename = args.save_path +'lidar/'+str(w_frame)+'.npy'
np.save(filename, lidar)

Zweitens, der Gesamtcode


import glob
import os
import sys
import time

try:
    sys.path.append(glob.glob('../carla/dist/carla-*%d.%d-%s.egg' % (
        sys.version_info.major,
        sys.version_info.minor,
        'win-amd64' if os.name == 'nt' else 'linux-x86_64'))[0])
except IndexError:
    pass

import carla
import numpy as np
import cv2
from queue import Queue, Empty
import copy
import random
random.seed(0)

# args
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--host', metavar='H',    default='127.0.0.1', help='IP of the host server (default: 127.0.0.1)')
parser.add_argument('--port', '-p',           default=2000, type=int, help='TCP port to listen to (default: 2000)')
parser.add_argument('--tm_port',              default=8000, type=int, help='Traffic Manager Port (default: 8000)')
parser.add_argument('--ego-spawn', type=list, default=None, help='[x,y] in world coordinate')
parser.add_argument('--top-view',             default=True, help='Setting spectator to top view on ego car')
parser.add_argument('--map',                  default='Town04', help='Town Map')
parser.add_argument('--sync',                 default=True, help='Synchronous mode execution')
parser.add_argument('--sensor-h',             default=2.4, help='Sensor Height')
parser.add_argument('--save-path',            default='储存路径', help='Synchronous mode execution')
args = parser.parse_args() 


actor_list, sensor_list = [], []
sensor_type = ['lidar']
def main(args):
    # We start creating the client
    client = carla.Client(args.host, args.port)
    client.set_timeout(5.0)
    
    world = client.get_world()
    # world = client.load_world('Town01')
    blueprint_library = world.get_blueprint_library()
    try:
        original_settings = world.get_settings()
        settings = world.get_settings()

        # We set CARLA syncronous mode
        settings.fixed_delta_seconds = 0.05
        settings.synchronous_mode = True
        world.apply_settings(settings)
        spectator = world.get_spectator()

        # 手动规定
        # transform_vehicle = carla.Transform(carla.Location(0, 10, 0), carla.Rotation(0, 0, 0))
        # 自动选择
        transform_vehicle = random.choice(world.get_map().get_spawn_points())
        ego_vehicle = world.spawn_actor(random.choice(blueprint_library.filter("model3")), transform_vehicle)
        actor_list.append(ego_vehicle)
        
        # 设置traffic manager
        tm = client.get_trafficmanager(args.tm_port)
        tm.set_synchronous_mode(True)
        # 是否忽略红绿灯
        # tm.ignore_lights_percentage(ego_vehicle, 100)
        # 如果限速30km/h -> 30*(1-10%)=27km/h
        tm.global_percentage_speed_difference(10.0)
        ego_vehicle.set_autopilot(True, tm.get_port())

        #-------------------------- 进入传感器部分 --------------------------#
        sensor_queue = Queue()
        lidar_bp = blueprint_library.find('sensor.lidar.ray_cast')

        lidar_bp.set_attribute('channels', '64')
        lidar_bp.set_attribute('points_per_second', '200000')
        lidar_bp.set_attribute('range', '64')
        lidar_bp.set_attribute('rotation_frequency','20') 
        lidar_bp.set_attribute('horizontal_fov', '360') 
        
        lidar01 = world.spawn_actor(lidar_bp, carla.Transform(carla.Location(z=args.sensor_h)), attach_to=ego_vehicle)
        lidar01.listen(lambda data: sensor_callback(data, sensor_queue, "lidar"))
        sensor_list.append(lidar01)

        
        #-------------------------- 传感器设置完毕 --------------------------#


        while True:
            # Tick the server
            world.tick()

            # 将CARLA界面摄像头跟随车动
            loc = ego_vehicle.get_transform().location
            spectator.set_transform(carla.Transform(carla.Location(x=loc.x,y=loc.y,z=15),carla.Rotation(yaw=0,pitch=-90,roll=0)))

            w_frame = world.get_snapshot().frame
            print("\nWorld's frame: %d" % w_frame)
            try:
                lidars = []
                for i in range (0, len(sensor_list)):
                    s_frame, s_name, s_data = sensor_queue.get(True, 1.0)
                    print("    Frame: %d   Sensor: %s" % (s_frame, s_name))
                    sensor_type = s_name.split('_')[0]
                    if sensor_type == 'lidar':
                        lidar = _parse_lidar_cb(s_data)

                cv2.imshow('vizs', visualize_data(lidar))
                cv2.waitKey(100)
                # if rgb is None or args.save_path is not None:
                    # 检查是否有各自传感器的文件夹
                mkdir_folder(args.save_path)

                filename = args.save_path +'lidar/'+str(w_frame)+'.npy'
                np.save(filename, lidar)

            except Empty:
                print("    Some of the sensor information is missed")

    finally:
        world.apply_settings(original_settings)
        tm.set_synchronous_mode(False)
        for sensor in sensor_list:
            sensor.destroy()
        for actor in actor_list:
            actor.destroy()
        print("All cleaned up!")

def mkdir_folder(path):
    for s_type in sensor_type:
        if not os.path.isdir(os.path.join(path, s_type)):
            os.makedirs(os.path.join(path, s_type))
    return True

def sensor_callback(sensor_data, sensor_queue, sensor_name):
    # Do stuff with the sensor_data data like save it to disk
    # Then you just need to add to the queue
    sensor_queue.put((sensor_data.frame, sensor_name, sensor_data))

# modify from world on rail code
def visualize_data(lidar, text_args=(0.6)):
    
    lidar_viz = lidar_to_bev(lidar).astype(np.uint8)
    lidar_viz = cv2.cvtColor(lidar_viz,cv2.COLOR_GRAY2RGB)

    return lidar_viz

# modify from world on rail code
def lidar_to_bev(lidar, min_x=-100,max_x=100,min_y=-100,max_y=100, pixels_per_meter=4, hist_max_per_pixel=2):
    xbins = np.linspace(
        min_x, max_x+1,
        (max_x - min_x) * pixels_per_meter + 1,
    )
    ybins = np.linspace(
        min_y, max_y+1,
        (max_y - min_y) * pixels_per_meter + 1,
    )
    # Compute histogram of x and y coordinates of points.
    hist = np.histogramdd(lidar[..., :2], bins=(xbins, ybins))[0]
    # Clip histogram
    hist[hist > hist_max_per_pixel] = hist_max_per_pixel
    # Normalize histogram by the maximum number of points in a bin we care about.
    overhead_splat = hist / hist_max_per_pixel * 255.
    # Return splat in X x Y orientation, with X parallel to car axis, Y perp, both parallel to ground.
    return overhead_splat[::-1,:]

# modify from leaderboard
def _parse_lidar_cb(lidar_data):
    points = np.frombuffer(lidar_data.raw_data, dtype=np.dtype('f4'))
    points = copy.deepcopy(points)
    points = np.reshape(points, (int(points.shape[0] / 4), 4))
    return points

if __name__ == "__main__":
    try:
        main(args)
    except KeyboardInterrupt:
        print(' - Exited by user.')

CARLA – Fahrzeug-Lidar-Installation – Daten anzeigen und speichern [super detailliert] – [Erste Schritte 4]

Verzeichnis der Serienartikel

Vorwort

1. Beschreibung des Demomoduls

3. Effektanzeige

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