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即时定位与地图构建 (SLAM) 是机器人所需的最基本功能之一。 由于图像唾手可得,视觉 SLAM (V-SLAM) 已成为许多自主系统的重要组成部分。 基于几何图形的方法和基于学习的方法都已取得令人瞩目的进展。 但是,为实际应用程序开发功能强大且可靠的 SLAM 方法仍是一个难题。 实际环境中存在很多难以处理的情况,例如光线改变或照明不足、动态物体以及结构不清的场景。 该数据集利用先进的计算机图形技术,旨在涵盖具有难以处理的特征的多种模拟场景。
备注
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此数据集是根据 Microsoft 接收源数据的原始条款提供的。 数据集可能包含来自 Microsoft 的数据。
数据是在存在多种光线条件、天气和移动物体的逼真模拟环境中收集的。 通过收集模拟数据,我们可以获取多模式传感器数据和精确的地面实况标签,包括立体 RGB 图像、深度图像、图像分割、光流和相机位姿。 我们设置了具有不同样式和场景的大量环境,涵盖难度颇大的视角和不同移动模式,这些是使用物理数据收集平台难以实现的。 我们的数据集的四个最重要的特征是:1) 大型多样化的现实数据;2) 多模态真实数据标签;3) 运动模式的多样性;4) 充满挑战的场景。
此数据集提供 5 种类型的数据,包括:
数据集从不同环境中收集数据,截至 2019 年包含总共几百个轨迹 (3 TB)。
在一些模拟中,数据集会模拟多种复杂的视觉效果。
此数据集存储在美国东部 Azure 区域。 建议将计算资源分配到美国东部地区,以实现相关性。
此项目在 MIT 许可下发布。 有关更多详细信息,请查看许可证文件。
查看官方 TartanAir 网站或查看原始研究论文。
如果对数据源有任何疑问,请发送电子邮件至 tartanair@hotmail.com。 也可通过相关的 GitHub 联系参与者。
AirSim 论文(FSR 2017 大会)中提供了 Citation More 的技术细节。 引用形式:
@article{tartanair2020arxiv,
title = {TartanAir: A Dataset to Push the Limits of Visual SLAM},
author = {Wenshan Wang, Delong Zhu, Xiangwei Wang, Yaoyu Hu, Yuheng Qiu, Chen Wang, Yafei Hu, Ashish Kapoor, Sebastian Scherer},
journal = {arXiv preprint arXiv:2003.14338},
year = {2020},
url = {https://arxiv.org/abs/2003.14338}
}
@inproceedings{airsim2017fsr,
author = {Shital Shah and Debadeepta Dey and Chris Lovett and Ashish Kapoor},
title = {AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles},
year = {2017},
booktitle = {Field and Service Robotics},
eprint = {arXiv:1705.05065},
url = {https://arxiv.org/abs/1705.05065}
}
使用以下代码示例访问 Python 笔记本中的数据。
pip install numpy
pip install azure-storage-blob
pip install opencv-python
from azure.storage.blob import ContainerClient
import numpy as np
import io
import cv2
import time
import matplotlib.pyplot as plt
%matplotlib inline
# Dataset website: http://theairlab.org/tartanair-dataset/
account_url = 'https://tartanair.blob.core.windows.net/'
container_name = 'tartanair-release1'
container_client = ContainerClient(account_url=account_url,
container_name=container_name,
credential=None)
0]
if trajname_split[-1][0] == 'P':
trajlist.append(trajname)
return trajlist
def _list_blobs_in_folder(folder_name):
"""
List all blobs in a virtual folder in an Azure blob container
"""
files = []
generator = container_client.list_blobs(name_starts_with=folder_name)
for blob in generator:
files.append(blob.name)
return files
def get_image_list(trajdir, left_right = 'left'):
assert(left_right == 'left' or left_right == 'right')
files = _list_blobs_in_folder(trajdir + '/image_' + left_right + '/')
files = [fn for fn in files if fn.endswith('.png')]
return files
def get_depth_list(trajdir, left_right = 'left'):
assert(left_right == 'left' or left_right == 'right')
files = _list_blobs_in_folder(trajdir + '/depth_' + left_right + '/')
files = [fn for fn in files if fn.endswith('.npy')]
return files
def get_flow_list(trajdir, ):
files = _list_blobs_in_folder(trajdir + '/flow/')
files = [fn for fn in files if fn.endswith('flow.npy')]
return files
def get_flow_mask_list(trajdir, ):
files = _list_blobs_in_folder(trajdir + '/flow/')
files = [fn for fn in files if fn.endswith('mask.npy')]
return files
def get_posefile(trajdir, left_right = 'left'):
assert(left_right == 'left' or left_right == 'right')
return trajdir + '/pose_' + left_right + '.txt'
def get_seg_list(trajdir, left_right = 'left'):
assert(left_right == 'left' or left_right == 'right')
files = _list_blobs_in_folder(trajdir + '/seg_' + left_right + '/')
files = [fn for fn in files if fn.endswith('.npy')]
return files
'>def get_environment_list():
'''
List all the environments shown in the root directory
'''
env_gen = container_client.walk_blobs()
envlist = []
for env in env_gen:
envlist.append(env.name)
return envlist
def get_trajectory_list(envname, easy_hard = 'Easy'):
'''
List all the trajectory folders, which is named as 'P0XX'
'''
assert(easy_hard=='Easy' or easy_hard=='Hard')
traj_gen = container_client.walk_blobs(name_starts_with=envname + '/' + easy_hard+'/')
trajlist = []
for traj in traj_gen:
trajname = traj.name
trajname_split = trajname.split('/')
trajname_split = [tt for tt in trajname_split if len(tt)>0]
if trajname_split[-1][0] == 'P':
trajlist.append(trajname)
return trajlist
def _list_blobs_in_folder(folder_name):
'''
List all blobs in a virtual folder in an Azure blob container
'''
files = []
generator = container_client.list_blobs(name_starts_with=folder_name)
for blob in generator:
files.append(blob.name)
return files
def get_image_list(trajdir, left_right = 'left'):
assert(left_right == 'left' or left_right == 'right')
files = _list_blobs_in_folder(trajdir + '/image_' + left_right + '/')
files = [fn for fn in files if fn.endswith('.png')]
return files
def get_depth_list(trajdir, left_right = 'left'):
assert(left_right == 'left' or left_right == 'right')
files = _list_blobs_in_folder(trajdir + '/depth_' + left_right + '/')
files = [fn for fn in files if fn.endswith('.npy')]
return files
def get_flow_list(trajdir, ):
files = _list_blobs_in_folder(trajdir + '/flow/')
files = [fn for fn in files if fn.endswith('flow.npy')]
return files
def get_flow_mask_list(trajdir, ):
files = _list_blobs_in_folder(trajdir + '/flow/')
files = [fn for fn in files if fn.endswith('mask.npy')]
return files
def get_posefile(trajdir, left_right = 'left'):
assert(left_right == 'left' or left_right == 'right')
return trajdir + '/pose_' + left_right + '.txt'
def get_seg_list(trajdir, left_right = 'left'):
assert(left_right == 'left' or left_right == 'right')
files = _list_blobs_in_folder(trajdir + '/seg_' + left_right + '/')
files = [fn for fn in files if fn.endswith('.npy')]
return files
envlist = get_environment_list()
print('Find {} environments..'.format(len(envlist)))
print(envlist)
diff_level = 'Easy'
env_ind = 0
trajlist = get_trajectory_list(envlist[env_ind], easy_hard = diff_level)
print('Find {} trajectories in {}'.format(len(trajlist), envlist[env_ind]+diff_level))
print(trajlist)
traj_ind = 1
traj_dir = trajlist[traj_ind]
left_img_list = get_image_list(traj_dir, left_right = 'left')
print('Find {} left images in {}'.format(len(left_img_list), traj_dir))
right_img_list = get_image_list(traj_dir, left_right = 'right')
print('Find {} right images in {}'.format(len(right_img_list), traj_dir))
left_depth_list = get_depth_list(traj_dir, left_right = 'left')
print('Find {} left depth files in {}'.format(len(left_depth_list), traj_dir))
right_depth_list = get_depth_list(traj_dir, left_right = 'right')
print('Find {} right depth files in {}'.format(len(right_depth_list), traj_dir))
left_seg_list = get_seg_list(traj_dir, left_right = 'left')
print('Find {} left segmentation files in {}'.format(len(left_seg_list), traj_dir))
right_seg_list = get_seg_list(traj_dir, left_right = 'left')
print('Find {} right segmentation files in {}'.format(len(right_seg_list), traj_dir))
flow_list = get_flow_list(traj_dir)
print('Find {} flow files in {}'.format(len(flow_list), traj_dir))
flow_mask_list = get_flow_mask_list(traj_dir)
print('Find {} flow mask files in {}'.format(len(flow_mask_list), traj_dir))
left_pose_file = get_posefile(traj_dir, left_right = 'left')
print('Left pose file: {}'.format(left_pose_file))
right_pose_file = get_posefile(traj_dir, left_right = 'right')
print('Right pose file: {}'.format(right_pose_file))
def read_numpy_file(numpy_file,):
'''
return a numpy array given the file path
'''
bc = container_client.get_blob_client(blob=numpy_file)
data = bc.download_blob()
ee = io.BytesIO(data.content_as_bytes())
ff = np.load(ee)
return ff
def read_image_file(image_file,):
'''
return a uint8 numpy array given the file path
'''
bc = container_client.get_blob_client(blob=image_file)
data = bc.download_blob()
ee = io.BytesIO(data.content_as_bytes())
img=cv2.imdecode(np.asarray(bytearray(ee.read()),dtype=np.uint8), cv2.IMREAD_COLOR)
im_rgb = img[:, :, [2, 1, 0]] # BGR2RGB
return im_rgb
def depth2vis(depth, maxthresh = 50):
depthvis = np.clip(depth,0,maxthresh)
depthvis = depthvis/maxthresh*255
depthvis = depthvis.astype(np.uint8)
depthvis = np.tile(depthvis.reshape(depthvis.shape+(1,)), (1,1,3))
return depthvis
def seg2vis(segnp):
colors = [(205, 92, 92), (0, 255, 0), (199, 21, 133), (32, 178, 170), (233, 150, 122), (0, 0, 255), (128, 0, 0), (255, 0, 0), (255, 0, 255), (176, 196, 222), (139, 0, 139), (102, 205, 170), (128, 0, 128), (0, 255, 255), (0, 255, 255), (127, 255, 212), (222, 184, 135), (128, 128, 0), (255, 99, 71), (0, 128, 0), (218, 165, 32), (100, 149, 237), (30, 144, 255), (255, 0, 255), (112, 128, 144), (72, 61, 139), (165, 42, 42), (0, 128, 128), (255, 255, 0), (255, 182, 193), (107, 142, 35), (0, 0, 128), (135, 206, 235), (128, 0, 0), (0, 0, 255), (160, 82, 45), (0, 128, 128), (128, 128, 0), (25, 25, 112), (255, 215, 0), (154, 205, 50), (205, 133, 63), (255, 140, 0), (220, 20, 60), (255, 20, 147), (95, 158, 160), (138, 43, 226), (127, 255, 0), (123, 104, 238), (255, 160, 122), (92, 205, 92),]
segvis = np.zeros(segnp.shape+(3,), dtype=np.uint8)
for k in range(256):
mask = segnp==k
colorind = k % len(colors)
if np.sum(mask)>0:
segvis[mask,:] = colors[colorind]
return segvis
def _calculate_angle_distance_from_du_dv(du, dv, flagDegree=False):
a = np.arctan2( dv, du )
angleShift = np.pi
if ( True == flagDegree ):
a = a / np.pi * 180
angleShift = 180
# print('Convert angle from radian to degree as demanded by the input file.')
d = np.sqrt( du * du + dv * dv )
return a, d, angleShift
def flow2vis(flownp, maxF=500.0, n=8, mask=None, hueMax=179, angShift=0.0):
'''
Show a optical flow field as the KITTI dataset does.
Some parts of this function is the transform of the original MATLAB code flow_to_color.m.
'''
ang, mag, _ = _calculate_angle_distance_from_du_dv( flownp[:, :, 0], flownp[:, :, 1], flagDegree=False )
# Use Hue, Saturation, Value colour model
hsv = np.zeros( ( ang.shape[0], ang.shape[1], 3 ) , dtype=np.float32)
am = ang < 0
ang[am] = ang[am] + np.pi * 2
hsv[ :, :, 0 ] = np.remainder( ( ang + angShift ) / (2*np.pi), 1 )
hsv[ :, :, 1 ] = mag / maxF * n
hsv[ :, :, 2 ] = (n - hsv[:, :, 1])/n
hsv[:, :, 0] = np.clip( hsv[:, :, 0], 0, 1 ) * hueMax
hsv[:, :, 1:3] = np.clip( hsv[:, :, 1:3], 0, 1 ) * 255
hsv = hsv.astype(np.uint8)
rgb = cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB)
if ( mask is not None ):
mask = mask > 0
rgb[mask] = np.array([0, 0 ,0], dtype=np.uint8)
return rgb
data_ind = 173 # randomly select one frame (data_ind < TRAJ_LEN)
left_img = read_image_file(left_img_list[data_ind])
right_img = read_image_file(right_img_list[data_ind])
# Visualize the left and right RGB images
plt.figure(figsize=(12, 5))
plt.subplot(121)
plt.imshow(left_img)
plt.title('Left Image')
plt.subplot(122)
plt.imshow(right_img)
plt.title('Right Image')
plt.show()
# Visualize the left and right depth files
left_depth = read_numpy_file(left_depth_list[data_ind])
left_depth_vis = depth2vis(left_depth)
right_depth = read_numpy_file(right_depth_list[data_ind])
right_depth_vis = depth2vis(right_depth)
plt.figure(figsize=(12, 5))
plt.subplot(121)
plt.imshow(left_depth_vis)
plt.title('Left Depth')
plt.subplot(122)
plt.imshow(right_depth_vis)
plt.title('Right Depth')
plt.show()
# Visualize the left and right segmentation files
left_seg = read_numpy_file(left_seg_list[data_ind])
left_seg_vis = seg2vis(left_seg)
right_seg = read_numpy_file(right_seg_list[data_ind])
right_seg_vis = seg2vis(right_seg)
plt.figure(figsize=(12, 5))
plt.subplot(121)
plt.imshow(left_seg_vis)
plt.title('Left Segmentation')
plt.subplot(122)
plt.imshow(right_seg_vis)
plt.title('Right Segmentation')
plt.show()
# Visualize the flow and mask files
flow = read_numpy_file(flow_list[data_ind])
flow_vis = flow2vis(flow)
flow_mask = read_numpy_file(flow_mask_list[data_ind])
flow_vis_w_mask = flow2vis(flow, mask = flow_mask)
plt.figure(figsize=(12, 5))
plt.subplot(121)
plt.imshow(flow_vis)
plt.title('Optical Flow')
plt.subplot(122)
plt.imshow(flow_vis_w_mask)
plt.title('Optical Flow w/ Mask')
plt.show()
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