相机参数请求

[a3124182425]

您好，我上次向您询问了LuSNAR数据集的相关相机参数，但是多次实验后很遗憾它是错的。我再次认真拜读您的文章没有找到相关信息。但看文章中提到有使用ORB-SLAM进行数据集验证，我根据您提供的参数进行实验，从轨迹预测结果上看在x,y,z三个方向有不同的尺度性误差。不知道您能否提供您在做ORB-SLAM时设置的config.yaml文件以作为参考。十分感谢您的帮助

[Zsy103]

以下是ORB-SLAM3所使用的配置文件：

%YAML:1.0
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------

# Camera calibration and distortion parameters (OpenCV) 
Camera.type: "PinHole"
Camera.fx: 610.177840000
Camera.fy: 610.177840000
Camera.cx: 512.0000000000000
Camera.cy: 512.0000000000000

Camera.k1: 0.0
Camera.k2: 0.0
Camera.p1: 0.0
Camera.p2: 0.0

Camera.width: 1024
Camera.height: 1024

# Camera frames per second 
Camera.fps: 10.0

# stereo baseline times fx
Camera.bf: 189.15513

# 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: 60.0

Tbc: !!opencv-matrix
   rows: 4
   cols: 4
   dt: f
#data: [0.0, 1.0, 0.0, 0.125,
          #-0.3420201433, 0.0, 0.9396926208, 0,
          #0.9396926208, 0.0, 0.3420201433, 0.0,
          #0.0, 0.0, 0.0, 1.0]
   data: [5.75395780e-17,  1.00000000e+00,  2.09426937e-17,  1.55000000e-01,
          -3.42020143e-01,  3.27925094e-33,  9.39692621e-01, -2.51559075e-01,
          9.39692621e-01, -6.12323400e-17,  3.42020143e-01, -5.73337594e-01,
          0.0, 0.0, 0.0, 1.0]

# IMU noise
IMU.NoiseGyro: 8.7266462e-4 # 1.6968e-04 
IMU.NoiseAcc: 0.02353596 # 2.0000e-3
IMU.GyroWalk: 9.9735023e-8 
IMU.AccWalk: 1.2481827e-6 # 3.0000e-3
IMU.Frequency: 100
#--------------------------------------------------------------------------------------------
# Stereo Rectification. Only if you need to pre-rectify the images.
# Camera.fx, .fy, etc must be the same as in LEFT.P
#--------------------------------------------------------------------------------------------
LEFT.height: 1024
LEFT.width: 1024
LEFT.D: !!opencv-matrix
   rows: 1
   cols: 5
   dt: d
   data: [0.0, 0.0, 0.0, 0.0, 0.0]
LEFT.K: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [610.177840000, 0.0, 512.000, 0.0, 610.177840000, 512.000 , 0.0, 0.0, 1.0]
LEFT.R:  !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0]
LEFT.P:  !!opencv-matrix
   rows: 3
   cols: 4
   dt: d
   data: [610.177840000, 0.0, 512.000, 0.0,  0.0, 610.177840000, 512.000, 0.0 , 0.0, 0.0, 1.0, 0.0]

RIGHT.height: 1024
RIGHT.width: 1024
RIGHT.D: !!opencv-matrix
   rows: 1
   cols: 5
   dt: d
   data: [0.0, 0.0, 0.0, 0.0, 0.0]
RIGHT.K: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [610.177840000, 0.0, 512.000, 0.0, 610.177840000, 512.000 , 0.0, 0.0, 1.0]
RIGHT.R:  !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0]
RIGHT.P:  !!opencv-matrix
   rows: 3
   cols: 4
   dt: d
   data: [610.177840000, 0.0, 512.000, -610.177840000,  0.0, 610.177840000, 512.000, 0.0 , 0.0, 0.0, 1.0, 0.0]

#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------

# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 600

# 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: 100
ORBextractor.minThFAST: 7

#--------------------------------------------------------------------------------------------
# Viewer Parameters
#--------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.05
Viewer.KeyFrameLineWidth: 1
Viewer.GraphLineWidth: 0.9
Viewer.PointSize: 2
Viewer.CameraSize: 0.08
Viewer.CameraLineWidth: 3
Viewer.ViewpointX: 0
Viewer.ViewpointY: -0.7
Viewer.ViewpointZ: -1.8
Viewer.ViewpointF: 500
Viewer.imageViewScale: 2

[Zsy103]

SLAM估计的位姿是初始化成功那一帧的左目相机坐标系，而数据集中提供的位姿真值是小车本体系在世界坐标系下的位姿，因此将SLAM结果直接与世界坐标系进行对比是不合理的，需要根据外参以及初始化成功那一帧的位姿真值，将所有SLAM估计的位姿都转换到世界系下。但是无论是否进行转换，如果是双目SLAM，尺度差异应该只是由累积误差产生的，短时间内是没有尺度差异的。你可以使用evo工具展示一下如下命令的结果：

  evo_traj tum --ref=gt.tum slam.tum -pva