Carla自动驾驶仿真十：Carlaviz三维可视化平台搭建

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前言

Carlaviz是一个开源的可视化工具，主要用于Carla三维场景、传感器数据以及自车数据的可视化，能够作为观测平台使用，本文主要介绍Carlaviz的安装以及基本使用；

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一、环境准备

1、docker安装

1）根据所属环境下载对应的docker，然后直接安装即可

点击进入docker官网下载

2、websocket-client安装

1）进入终端输入：pip3 install websocket_client

3、carlaviz代码下载

carlaviz github链接

1）打开终端输入 docker pull mjxu96/carlaviz:0.9.14，请下载与自己carla版本一致的carlaviz，只需修改后面的版本号，如下载0.9.15版本的carlaviz：

二、carlaviz使用

1、打开carla客户端

2、输入启动命令

1）windows
终端输入：docker run -it -p 8080-8081:8080-8081 mjxu96/carlaviz:0.9.14 --simulator_host host.docker.internal --simulator_port 2000，注意carla的版本号一定要对上；

2）linux
终端输入：docker run -it --network="host" mjxu96/carlaviz:0.9.14 --simulator_host localhost --simulator_port 2000，注意carla的版本号一定要对上‘

windows输入启动命令后结果：

3、进入carlaviz

1）打开浏览器输入http://localhost:8080/，或者从docker软件进入，进入carlaviz如下图所示，能够正确加载到路网相关信息，此时没有ego信息以及摄像头画面是正常的，是因为需要启动python脚本生成车辆以及摄像头；

4、修改manual_control.py脚本

1、启动前需要将manual_control.py中主车的名称改成ego

5、运行manual_control.py脚本

1）运行脚本后正确接收到主车信息，摄像头画面等信息；

6、运行carlaviz官方脚本（推荐）

1）我们也可以运行官方脚本，有激光雷达点云信息；

import carla
import random
import time
# from carla_painter import CarlaPainterdef do_something(data):passdef main():try:# initialize one painter# painter = CarlaPainter('localhost', 8089)client = carla.Client('localhost', 2000)client.set_timeout(10.0)world = client.get_world()for blue_print in world.get_blueprint_library():if blue_print.id.startswith("sensor"):print(blue_print)# set synchronous modeprevious_settings = world.get_settings()world.apply_settings(carla.WorldSettings(synchronous_mode=True,fixed_delta_seconds=1.0 / 30.0))# randomly spawn an ego vehicle and several other vehiclesspawn_points = world.get_map().get_spawn_points()blueprints_vehicles = world.get_blueprint_library().filter("vehicle.*")ego_transform = spawn_points[random.randint(0, len(spawn_points) - 1)]other_vehicles_transforms = []for _ in range(3):other_vehicles_transforms.append(spawn_points[random.randint(0, len(spawn_points) - 1)])blueprints_vehicles = [x for x in blueprints_vehicles if int(x.get_attribute('number_of_wheels')) == 4]# set ego vehicle's role name to let CarlaViz know this vehicle is the ego vehicleblueprints_vehicles[0].set_attribute('role_name', 'ego') # or set to 'hero'batch = [carla.command.SpawnActor(blueprints_vehicles[0], ego_transform).then(carla.command.SetAutopilot(carla.command.FutureActor, True))]results = client.apply_batch_sync(batch, True)if not results[0].error:ego_vehicle = world.get_actor(results[0].actor_id)else:print('spawn ego error, exit')ego_vehicle = Nonereturnother_vehicles = []batch = []for i in range(3):batch.append(carla.command.SpawnActor(blueprints_vehicles[i + 1], other_vehicles_transforms[i]).then(carla.command.SetAutopilot(carla.command.FutureActor, True)))# set autopilot for all these actorsego_vehicle.set_autopilot(True)results = client.apply_batch_sync(batch, True)for result in results:if not result.error:other_vehicles.append(result.actor_id)# attach a camera and a lidar to the ego vehiclecamera = None# blueprint_camera = world.get_blueprint_library().find('sensor.camera.rgb')blueprint_camera = world.get_blueprint_library().find('sensor.camera.instance_segmentation')# blueprint_camera = world.get_blueprint_library().find('sensor.camera.depth')blueprint_camera.set_attribute('image_size_x', '640')blueprint_camera.set_attribute('image_size_y', '480')blueprint_camera.set_attribute('fov', '110')blueprint_camera.set_attribute('sensor_tick', '0.1')transform_camera = carla.Transform(carla.Location(y=+3.0, z=5.0))camera = world.spawn_actor(blueprint_camera, transform_camera, attach_to=ego_vehicle)camera.listen(lambda data: do_something(data))lidar = None# blueprint_lidar = world.get_blueprint_library().find('sensor.lidar.ray_cast')blueprint_lidar = world.get_blueprint_library().find('sensor.lidar.ray_cast_semantic')blueprint_lidar.set_attribute('range', '30')blueprint_lidar.set_attribute('rotation_frequency', '10')blueprint_lidar.set_attribute('channels', '32')blueprint_lidar.set_attribute('lower_fov', '-30')blueprint_lidar.set_attribute('upper_fov', '30')blueprint_lidar.set_attribute('points_per_second', '56000')transform_lidar = carla.Transform(carla.Location(x=0.0, z=5.0))lidar = world.spawn_actor(blueprint_lidar, transform_lidar, attach_to=ego_vehicle)lidar.listen(lambda data: do_something(data))# tick to generate these actors in the game worldworld.tick()# save vehicles' trajectories to draw in the frontendtrajectories = [[]]while (True):world.tick()ego_location = ego_vehicle.get_location()trajectories[0].append([ego_location.x, ego_location.y, ego_location.z])# draw trajectories# painter.draw_polylines(trajectories)# draw ego vehicle's velocity just above the ego vehicleego_velocity = ego_vehicle.get_velocity()velocity_str = "{:.2f}, ".format(ego_velocity.x) + "{:.2f}".format(ego_velocity.y) \+ ", {:.2f}".format(ego_velocity.z)# painter.draw_texts([velocity_str],#             [[ego_location.x, ego_location.y, ego_location.z + 10.0]], size=20)time.sleep(0.05)finally:if previous_settings is not None:world.apply_settings(previous_settings)if lidar is not None:lidar.stop()lidar.destroy()if camera is not None:camera.stop()camera.destroy()if ego_vehicle is not None:ego_vehicle.destroy()if other_vehicles is not None:client.apply_batch([carla.command.DestroyActor(x) for x in other_vehicles])if __name__ == "__main__":

综上，完成carlaviz的安装及使用，确实是一个较只管的观测平台，如果能在基础上做控制的开发那就完美了。

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