一、数据录制
数据录制的部分参考了网上的部分代码，代码本身并不复杂，基本都是简单的CARLA语法，关键的一点在于，CARLA内部本身并没有预设的双目摄像头，需要我们添加两个朝向相同的摄像头来组成双目系统，这一点体现在添加相机时的位置和角度。之后利用回调函数进行保存，为了方便构建真值信息，这里我同时添加了gnss和imu的信息。为了让录制过程车辆不会停下来等红绿灯，交通管理的部分已经注释掉了。代码如下：
#!/usr/bin/env python
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:passimport carla
import random
import numpy as np
import cv2
from queue import Queue, Empty
import copy
import random
random.seed(0)
from agents.navigation.basic_agent import BasicAgent
from agents.navigation.behavior_agent import BehaviorAgent # 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='Town10HD_Opt', 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='/home/zhihe/Documents/Dataset/CARLA/Town10/', help='Synchronous mode execution')
parser.add_argument('--behavior', type=str, default='normal', help='Choose one of the possible agent behaviors')
args = parser.parse_args()IM_WIDTH = 1392
IM_HEIGHT = 512actor_list, sensor_list = [], []
sensor_type = ['rgb','lidar','imu','gnss']
def main(args):client = carla.Client(args.host, args.port)client.set_timeout(5.0)traffic_manager = client.get_trafficmanager()# world = client.get_world()world_name = args.mapworld = client.load_world(world_name)# 获取所有的交通灯traffic_lights = world.get_actors().filter('traffic.traffic_light')# 将所有交通灯设置为绿灯并冻结状态for traffic_light in traffic_lights:traffic_light.set_state(carla.TrafficLightState.Green)traffic_light.freeze(True) # 冻结当前状态，保持绿灯不变blueprint_library = world.get_blueprint_library()try:original_settings = world.get_settings()settings = world.get_settings()settings.fixed_delta_seconds = 0.05settings.synchronous_mode = Trueworld.apply_settings(settings)traffic_manager.set_synchronous_mode(True)spectator = world.get_spectator()points_in_map = world.get_map().get_spawn_points()start_position = points_in_map[56]end_position = points_in_map[84]ego_vehicle = world.spawn_actor(random.choice(blueprint_library.filter("model3")), start_position)actor_list.append(ego_vehicle)if args.sync:world.tick()else:world.wait_for_tick()physics_control = ego_vehicle.get_physics_control()physics_control.use_sweep_wheel_collision = Trueego_vehicle.apply_physics_control(physics_control)#-------------------------- 进入传感器部分 --------------------------#sensor_queue = Queue()cam_bp = blueprint_library.find('sensor.camera.rgb')# lidar_bp = blueprint_library.find('sensor.lidar.ray_cast')imu_bp = blueprint_library.find('sensor.other.imu')gnss_bp = blueprint_library.find('sensor.other.gnss')cam_bp.set_attribute("image_size_x", "{}".format(IM_WIDTH))cam_bp.set_attribute("image_size_y", "{}".format(IM_HEIGHT))cam_bp.set_attribute("fov", "60")# cam_bp.set_attribute('sensor_tick', '0.1')cam01 = world.spawn_actor(cam_bp, carla.Transform(carla.Location(y=-1, z=args.sensor_h),carla.Rotation(yaw=0)), attach_to=ego_vehicle)cam01.listen(lambda data: sensor_callback(data, sensor_queue, "rgb_left"))sensor_list.append(cam01)cam02 = world.spawn_actor(cam_bp, carla.Transform(carla.Location(y=1, z=args.sensor_h),carla.Rotation(yaw=0)), attach_to=ego_vehicle)cam02.listen(lambda data: sensor_callback(data, sensor_queue, "rgb_right"))sensor_list.append(cam02)# lidar_bp.set_attribute('channels', '64')# lidar_bp.set_attribute('points_per_second', '200000')# lidar_bp.set_attribute('range', '32')# lidar_bp.set_attribute('rotation_frequency', str(int(1/settings.fixed_delta_seconds))) # 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)imu01 = world.spawn_actor(imu_bp, carla.Transform(carla.Location(z=args.sensor_h)), attach_to=ego_vehicle)imu01.listen(lambda data: sensor_callback(data, sensor_queue, "imu"))sensor_list.append(imu01)gnss01 = world.spawn_actor(gnss_bp, carla.Transform(carla.Location(z=args.sensor_h)), attach_to=ego_vehicle)gnss01.listen(lambda data: sensor_callback(data, sensor_queue, "gnss"))sensor_list.append(gnss01)#-------------------------- 传感器设置完毕 --------------------------## 清空文档file_path = args.save_path +'imu/'+str(args.map)+'.txt'with open(file_path, 'w') as file:file.write("") file_path = args.save_path +'gnss/'+str(args.map)+'.txt'with open(file_path, 'w') as file:file.write("")# 指定要清空图片文件的路径directory = args.save_path +'rgb/image_left'file_list = os.listdir(directory)# 筛选出所有的图片文件并删除for file in file_list:if file.lower().endswith(('.png', '.jpg', '.jpeg', '.gif', '.bmp')):file_path = os.path.join(directory, file)os.remove(file_path)directory = args.save_path +'rgb/image_right'file_list = os.listdir(directory)# 筛选出所有的图片文件并删除for file in file_list:if file.lower().endswith(('.png', '.jpg', '.jpeg', '.gif', '.bmp')):file_path = os.path.join(directory, file)os.remove(file_path)agent = BehaviorAgent(ego_vehicle, behavior=args.behavior)agent.set_destination(end_position.location)while True:# Tick the server# agent.update_information(ego_vehicle)world.tick()# 将CARLA界面摄像头跟随车动loc = ego_vehicle.get_transform().locationspectator.set_transform(carla.Transform(carla.Location(x=loc.x,y=loc.y,z=35),carla.Rotation(yaw=0,pitch=-90,roll=0)))w_frame = world.get_snapshot().frameprint("\nWorld's frame: %d" % w_frame)try:rgbs_left = []rgbs_right = []rgb_timestamp = 0for 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 s_name == 'rgb_left':rgb_timestamp = s_data.timestamprgbs_left.append(_parse_image_cb(s_data))elif s_name == 'rgb_right':rgb_timestamp = s_data.timestamprgbs_right.append(_parse_image_cb(s_data))elif s_name == 'lidar':lidar = _parse_lidar_cb(s_data)elif s_name == 'imu':imu = s_dataelif s_name == 'gnss':gnss = s_data# 仅用来可视化 可注释rgb_left = np.concatenate(rgbs_left, axis=1)[...,:3]rgb_right = np.concatenate(rgbs_right, axis=1)# cv2.imshow('vizs', visualize_data(rgb, imu_yaw, gnss))# cv2.imshow('vizs', visualize_data(rgb, lidar, imu_yaw, gnss))# cv2.waitKey(100)mkdir_folder(args.save_path)if rgb_left is None or args.save_path is not None:filename = args.save_path +'rgb/image_left/'+str(rgb_timestamp)+'.png'cv2.imwrite(filename, np.array(rgb_left[...,::-1]))if rgb_right is None or args.save_path is not None:filename = args.save_path +'rgb/image_right/'+str(rgb_timestamp)+'.png'cv2.imwrite(filename, np.array(rgb_right[...,::-1]))# filename = args.save_path +'lidar/'+str(w_frame)+'.npy'# np.save(filename, lidar)if imu is None or args.save_path is not None:file_path = args.save_path +'imu/'+str(args.map)+'.txt'with open(file_path, 'a') as file:file.write(str(imu.timestamp)+' '+str(imu.gyroscope.y)+' '+str(imu.gyroscope.x)+' '+str(imu.gyroscope.z)+'\n')if gnss is None or args.save_path is not None:file_path = args.save_path +'gnss/'+str(args.map)+'.txt'with open(file_path, 'a') as file:file.write(str(gnss.timestamp)+' '+str(gnss.latitude)+' '+str(gnss.longitude)+' '+str(gnss.altitude)+'\n')except Empty:print(" Some of the sensor information is missed")if (agent.done()):breakcontrol = agent.run_step()control.manual_gear_shift = Falseego_vehicle.apply_control(control)finally:world.apply_settings(original_settings)traffic_manager.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 Truedef 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 queuesensor_queue.put((sensor_data.frame, sensor_name, sensor_data))# modify from world on rail code
# def visualize_data(rgb, lidar, imu_yaw, gnss, text_args=(cv2.FONT_HERSHEY_SIMPLEX, 0.3, (255,255,255), 1)):# canvas = np.array(rgb[...,::-1])# if lidar is not None:
# lidar_viz = lidar_to_bev(lidar).astype(np.uint8)
# lidar_viz = cv2.cvtColor(lidar_viz,cv2.COLOR_GRAY2RGB)
# canvas = np.concatenate([canvas, cv2.resize(lidar_viz.astype(np.uint8), (canvas.shape[0], canvas.shape[0]))], axis=1)# # cv2.putText(canvas, f'yaw angle: {imu_yaw:.3f}', (4, 10), *text_args)
# # cv2.putText(canvas, f'log: {gnss[0]:.3f} alt: {gnss[1]:.3f} brake: {gnss[2]:.3f}', (4, 20), *text_args)# return canvas
def visualize_data(rgb, imu_yaw, gnss, text_args=(cv2.FONT_HERSHEY_SIMPLEX, 0.3, (255,255,255), 1)):canvas = np.array(rgb[...,::-1])# if lidar is not None:# lidar_viz = lidar_to_bev(lidar).astype(np.uint8)# lidar_viz = cv2.cvtColor(lidar_viz,cv2.COLOR_GRAY2RGB)# canvas = np.concatenate([canvas, cv2.resize(lidar_viz.astype(np.uint8), (canvas.shape[0], canvas.shape[0]))], axis=1)# cv2.putText(canvas, f'yaw angle: {imu_yaw:.3f}', (4, 10), *text_args)# cv2.putText(canvas, f'log: {gnss[0]:.3f} alt: {gnss[1]:.3f} brake: {gnss[2]:.3f}', (4, 20), *text_args)return canvas
# modify from world on rail code
def lidar_to_bev(lidar, min_x=-24,max_x=24,min_y=-16,max_y=16, pixels_per_meter=4, hist_max_per_pixel=10):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 histogramhist[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 manual control
def _parse_image_cb(image):array = np.frombuffer(image.raw_data, dtype=np.dtype("uint8"))array = np.reshape(array, (image.height, image.width, 4))array = array[:, :, :3]array = array[:, :, ::-1]return array
# 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 pointsif __name__ == "__main__":try:main(args)except KeyboardInterrupt:print(' - Exited by user.')
为了方便重复录制数据，这里我是使用预设的地图点作为起点和终点，为了方便查看当前地图的所有地图点，可以使用下面的代码进行所有地图点的可视化。
#!/usr/bin/env python# Copyright (c) 2018 Intel Labs.
# authors: German Ros (german.ros@intel.com)
#
# This work is licensed under the terms of the MIT license.
# For a copy, see <https://opensource.org/licenses/MIT>."""Example of automatic vehicle control from client side."""from __future__ import print_functionimport argparse
import collections
import datetime
import glob
import logging
import math
import os
import numpy.random as random
import re
import sys
import weakref
import cv2try:import pygamefrom pygame.locals import KMOD_CTRLfrom pygame.locals import K_ESCAPEfrom pygame.locals import K_q
except ImportError:raise RuntimeError('cannot import pygame, make sure pygame package is installed')try:import numpy as np
except ImportError:raise RuntimeError('cannot import numpy, make sure numpy package is installed')# ==============================================================================
# -- Find CARLA module ---------------------------------------------------------
# ==============================================================================
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# ==============================================================================
# -- Add PythonAPI for release mode --------------------------------------------
# ==============================================================================
try:sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + '/carla')
except IndexError:passfrom carla import ColorConverter as ccfrom agents.navigation.behavior_agent import BehaviorAgent # pylint: disable=import-error
from agents.navigation.basic_agent import BasicAgent # pylint: disable=import-errorimport carlaclient = carla.Client('localhost',2000)
world = client.get_world()
#world.set_weather(world.get_weather().ClearNight)
m = world.get_map()
transform = carla.Transform()
spectator = world.get_spectator()
bv_transform = carla.Transform(transform.location + carla.Location(z=250,x=0), carla.Rotation(yaw=0, pitch=-90))
spectator.set_transform(bv_transform)blueprint_library = world.get_blueprint_library()
spawn_points = m.get_spawn_points()for i, spawn_point in enumerate(spawn_points):world.debug.draw_string(spawn_point.location, str(i), life_time=100)world.debug.draw_arrow(spawn_point.location, spawn_point.location + spawn_point.get_forward_vector(), life_time=100)while True:world.wait_for_tick()
二、真值处理
录制过程记录的imu和gnss信息需要进行处理才能拼接成tum格式的groundtruth。下面代码是用于拼接imu和gnss的代码，这里默认imu输出的是时间戳和rpy角度，gnss输出的是时间戳和xyz坐标、转换为tum格式时，rpy角会被转换为四元数形式，而xyz按道理是可以直接用，但是拼接后容易出现无法和SLAM结果对齐的问题，这里还是建议根据SLAM运行结果，调整xyz的顺序。
import numpy as np
from scipy.spatial.transform import Rotation as R# 定义函数，将 RPY 转换为四元数
def rpy_to_quaternion(roll, pitch, yaw):r = R.from_euler('xyz', [roll, pitch, yaw], degrees=False)q = r.as_quat() # [qx, qy, qz, qw]return q[0], q[1], q[2], q[3]# 读取 IMU 文件和 GNSS 文件
imu_data = np.loadtxt('./Town10/imu/Town10HD_Opt.txt')
gnss_data = np.loadtxt('./Town10/gnss/Town10HD_Opt.txt')# 检查时间戳一致性
assert np.array_equal(imu_data[:, 0], gnss_data[:, 0]), "时间戳不一致"# 初始化保存轨迹真值的数据列表
trajectory = []# 遍历每一行数据
for i in range(len(imu_data)):timestamp = imu_data[i, 0]roll, pitch, yaw = imu_data[i, 1], imu_data[i, 2], imu_data[i, 3]tx, ty, tz = gnss_data[i, 1], gnss_data[i, 2], gnss_data[i, 3]# 转换 RPY 为四元数qx, qy, qz, qw = rpy_to_quaternion(roll, pitch, yaw)# 组合数据并添加到轨迹列表trajectory.append([timestamp, 100000*tx, 0*tz, 100000*ty, qx, qy, qz, qw])# 保存结果到新文件
np.savetxt('groundtruth.txt', trajectory, fmt='%.6f', delimiter=' ', header="timestamp tx ty tz qx qy qz qw")三、使用ORBSLAM2运行自建CARLA数据集
在使用ORBSLAM2运行自建数据集时，首先需要自己构建一个time.txt，不然会出现无法读取图像的问题，我在录制数据集时采用的是将时间戳作为文件名，这会导致直接转换为double时溢出，所以这里我限制了六位的长度。
import osdef save_sorted_and_rename_files(directory_path, output_file):# 获取目录下所有以 .png 结尾的文件名filenames = [f for f in os.listdir(directory_path) if f.endswith('.png') and os.path.isfile(os.path.join(directory_path, f))]# 将文件名去掉 .png 后缀并转换为浮点数，再按浮点数排序filenames = sorted(filenames, key=lambda x: float(x.replace('.png', '')))# 重命名文件并写入排序后的文件名到txt文件with open(output_file, 'w') as file:for original_filename in filenames:# 去掉 .png 后缀并保留小数点后六位try:float_value = float(original_filename.replace('.png', ''))new_filename = f"{float_value:.6f}.png"# 重命名文件original_path = os.path.join(directory_path, original_filename)new_path = os.path.join(directory_path, new_filename)os.rename(original_path, new_path)# 写入新的文件名（不带扩展名）file.write(f"{new_filename.replace('.png', '')}\n")except ValueError:# 跳过无法转换为浮点数的文件名continue# 示例用法
directory_path = './Town10/rgb/image_left/' # 替换为你的目录路径
output_file = './Town10/rgb/times.txt' # 输出文件名save_sorted_and_rename_files(directory_path, output_file)除此之外，比较关键的一点是carla中相机的内外参，在仿真环境中添加标定板显然是不现实。在carla的github评论区找到了一个解决方法，这个方法实际上在官方的演示文件里面也用到了。计算内参可以使用下面的公式：
Focus_length = ImageSizeX /(2 * tan(CameraFOV * π / 360))
Center_X = ImageSizeX / 2
Center_Y = ImageSizeY / 2
其中Focus_length为焦距f，Center_X和Center_Y分别为cx和cy，CameraFOV要根据录制数据时的设置进行调整，我前面代码使用的FOV是60。而外参是根据添加相机时位置的设置计算出来的，我前面代码中两个相机朝向相同差别只在y上，根据carla的单位，这里相当于y上差了两米，即基线距离为2m，而orbslam的yaml文件中，Camera.bf字段的单位并不是米，而是基线距离乘以焦距，所以这里我们需要再2m的基础上再乘以前面计算出的焦距。最后修改出来的配置文件为：
%YAML:1.0#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 1206
Camera.fy: 1206
Camera.cx: 696
Camera.cy: 256Camera.k1: 0.0
Camera.k2: 0.0
Camera.p1: 0.0
Camera.p2: 0.0Camera.width: 1392
Camera.height: 512# Camera frames per second
Camera.fps: 20.0# stereo baseline times fx
Camera.bf: 2412# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1# Close/Far threshold. Baseline times.
ThDepth: 40#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 2000# ORB Extractor: Scale factor between levels in the scale pyramid
ORBextractor.scaleFactor: 1.2# ORB Extractor: Number of levels in the scale pyramid
ORBextractor.nLevels: 8# ORB Extractor: Fast threshold
# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
# You can lower these values if your images have low contrast
ORBextractor.iniThFAST: 12
ORBextractor.minThFAST: 7#--------------------------------------------------------------------------------------------
# Viewer Parameters
#--------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.6
Viewer.KeyFrameLineWidth: 2
Viewer.GraphLineWidth: 1
Viewer.PointSize:2
Viewer.CameraSize: 0.7
Viewer.CameraLineWidth: 3
Viewer.ViewpointX: 0
Viewer.ViewpointY: -100
Viewer.ViewpointZ: -0.1
Viewer.ViewpointF: 2000如果配置文件不对，ORBSLAM的运行结果会出现很大差别，尤其是在转弯的时候，如果内外参错了会直接导致转弯偏离巨大。
最后放一张成功运行并使用evo进行评价的图：
