相关解析及参考：
超详细解读ORB-SLAM3单目初始化（下篇）
ORB_SLAM3和之前版本有什么不同？_小白学视觉的技术博客_51CTO博客
orbslam3 官方源码地址：https://github.com/UZ-SLAMLab/ORB_SLAM3
‼️ 注意如果是ROS编译请见issue：https://github.com/UZ-SLAMLab/ORB_SLAM3/issues/442 或直接使用fork版本下的 https://gitee.com/kin_zhang/ORB_SLAM3/tree/fix%2Fros_build/
此记录仅为小白式探索记录，主要是用自己的数据集跑一下orbslam3，之前对此并不了解，所以整篇内容会较为小白式，所有的参考解析均在前言部分给出 或途中给出，主要就是运行一下 在自己设备上跑一跑 看看效果 【然后发现emmm IMU是个坑 🙃】
在18.04 Docker和 20.04本机 下均进行了尝试，因为Docker内部无法把 Pangolin 显示给映射过来，只能走ros master节点，所以 最后又在本机上进行了尝试
如果是走ROS的话，安装完ROS 基本只需要装一个pangolin进行显示用
安装方法见 ‣，或如下
# Clone Pangolin along with it's submodules
git clone --recursive https://github.com/stevenlovegrove/Pangolin.git
cd Pangolin
# Install dependencies (as described above, or your preferred method)
./scripts/install_prerequisites.sh recommended
# Configure and build
cmake -B build
cmake --build build
# GIVEME THE PYTHON STUFF!!!! (Check the output to verify selected python version)
cmake --build build -t pypangolin_pip_install
然后ubuntu 20.04 需要进行一些修改建议直接clone fork版本：
git clone -b feat/20.04 https://github.com/kin-zhang/ORB_SLAM3.git
cd ORB_SLAM3
chmod +x build.sh
./build.sh
# ---- 记得换PATH哦！！！
export ROS_PACKAGE_PATH=${ROS_PACKAGE_PATH}:PATH/ORB_SLAM3/Examples/ROS
chmod +x build_ros.sh
./build_ros.sh
# ---- examples
export ROS_PACKAGE_PATH=${ROS_PACKAGE_PATH}:/home/kin/workspace/ORB_SLAM3/Examples_old/ROS
chmod +x build_ros.sh
./build_ros.sh
相关修改：
之后就ok了，可以直接测试提供的数据集 ，或者 阿里盘链接 （好像不可分享 emmm）
然后运行即可 【需要开三个终端】
roscore
rosrun ORB_SLAM3 Stereo_Inertial Vocabulary/ORBvoc.txt Examples_old/Stereo-Inertial/EuRoC.yaml true
rosbag play --pause V1_02_medium.bag /cam0/image_raw:=/camera/left/image_raw /cam1/image_raw:=/camera/right/image_raw /imu0:=/imu
运行示意：
后续部分主要针对 如果想跑自己的数据集的相关记录
因为是自己的数据集，所以需要 自己进行标定，其实原repo里有个pdf写的挺好的，以下为单目 不带IMU为例（因为这样简单…）
Examples 下有各个对应相机（单目、单目+IMU、深度、深度+IMU、双目、双目+IMU）一些示例的yaml脚本，我们打开 Examples_old/Monocular/EuRoC.yaml
对于单目来说，只需要标定以下内参即可：
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 458.654
Camera.fy: 457.296
Camera.cx: 367.215
Camera.cy: 248.375
Camera.k1: -0.28340811
Camera.k2: 0.07395907
Camera.p1: 0.00019359
Camera.p2: 1.76187114e-05
Camera.width: 752
Camera.height: 480
# Camera frames per second
Camera.fps: 20.0
内参为 focal length (fx, fy) 和 central point (cx, cy) ，还有就是distortion model (k1, k2, (k3,k4) p1, p2, (p3,p4))
ROS标定 wiki
rosrun camera_calibration cameracalibrator.py --size 7x10 --square 0.5 image:=/camera/image_raw --no-service-check
matlab标定CSDN
一般情况下会得到这样一组 intrinsic matrix，相关原理及介绍
K = ( f x s x 0 0 f y y 0 0 0 1 ) K=\left(\begin{array}{ccc}f_{x} & s & x_{0} \\0 & f_{y} & y_{0} \\0 & 0 & 1\end{array}\right) K=⎝⎛fx00sfy0x0y01⎠⎞
可以注意到两边矩阵有时候需要转置一下（一般opencv遵循上面公式所对应的）
比如自己的数据集给出的矩阵是这样的，然后读取了bag包知道 fps也是20hz
image_width: 1024
image_height: 768
camera_name: stereo_right_Flir_BFSU3
camera_matrix: !!opencv-matrix
rows: 3
cols: 3
dt: f
data: [ 6.04964966e+02, 0., 5.17844666e+02,
0., 6.04625610e+02, 3.89209320e+02,
0., 0., 1. ]
distortion_model: plumb_bob
distortion_coefficients: !!opencv-matrix
rows: 1
cols: 4
dt: f
data: [ -9.58003029e-02, 8.74120295e-02, 2.08094658e-04, -1.08567670e-04 ]
rectification_matrix: !!opencv-matrix
rows: 3
cols: 3
dt: f
data: [ 9.99987543e-01, 4.83056623e-03, 1.24577642e-03,
-4.83735651e-03, 9.99973118e-01, 5.50653692e-03,
-1.21914328e-03, -5.51249459e-03, 9.99984086e-01 ]
对照起来复制一个 EuRoC.yaml 修改一下 KIN.yaml ：
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 604.964
Camera.fy: 604.625
Camera.cx: 517.844
Camera.cy: 389.2093
Camera.k1: -0.0958
Camera.k2: 0.08741
Camera.p1: 2.08094658e-04
Camera.p2: -1.08567670e-04
Camera.width: 1024
Camera.height: 768
# Camera frames per second
Camera.fps: 20.0
主要是双目相机/双相机情况下，需要修改外参
比如这里给出了 左右相机 到 IMU的（有时间也可直接标定c1_c2的转换 不通过body）
# 左相机 c1
# extrinsics from the sensor (reference) to bodyimu (target)
quaternion_sensor_bodyimu: !!opencv-matrix
rows: 1
cols: 4
dt: f
data: [0.501677, 0.491365, -0.508060, 0.498754]
translation_sensor_bodyimu: !!opencv-matrix
rows: 1
cols: 3
dt: f
data: [0.066447, -0.019381, -0.077907]
# 右相机 c2
# extrinsics from the sensor (reference) to bodyimu (target)
quaternion_sensor_bodyimu: !!opencv-matrix
rows: 1
cols: 4
dt: f
data: [0.495420, 0.501199, -0.503827, 0.499516]
translation_sensor_bodyimu: !!opencv-matrix
rows: 1
cols: 3
dt: f
data: [-0.093388, -0.017886, -0.078768]
然后我们直接复制到matlab得到T_c1_c2 （从坐标c1到c2）
T_c1_b = trvec2tform([0.066447, -0.019381, -0.077907])*quat2tform([0.501677, 0.491365, -0.508060, 0.498754])
T_c2_b = trvec2tform([-0.093388, -0.017886, -0.078768])*quat2tform([0.495420, 0.501199, -0.503827, 0.499516])
disp(T_c1_b*inv(T_c2_b))
然后得到一个4x4的矩阵 复制到 Stereo 文件夹下替换即可（当然两个内参也需要换一下）
Stereo.ThDepth: 60.0
Stereo.T_c1_c2: !!opencv-matrix
rows: 4
cols: 4
dt: f
data: [1.0071,0.0073,-0.00140,0.0798,
-0.0051,1.0161,0.0164,-0.0002,
-0.0020,-0.0091,1.0019,0.0003,
0,0,0,1.0000]
单目基本只需要改一下相机参数，就可以了 更多的关于orbslam方面相关参数
其中因为自身数据录制的时候 image的type是 sensor_msgs/CompressedImage 需要做一下转换
roscore
rosrun image_transport republish compressed in:=/stereo/frame_right/image_raw raw out:=/camera/image_raw
然后再跑数据包
rosbag play --pause 20220216_MCR_normal.bag /imu:=/stim300/imu/data_raw
然后再跑算法
rosrun ORB_SLAM3 Mono Vocabulary/ORBvoc.txt Examples_old/Monocular/KIN.yaml
运行示例截图：
双目需要改两个相机的内参，同时给出c1相机到c2的 homogeneous transformation 矩阵。
然后和单目的差不多 只是换一下算法名和republish的topic
roscore
rosrun image_transport republish compressed in:=/stereo/frame_left/image_raw raw out:=/camera/left/image_raw
rosrun image_transport republish compressed in:=/stereo/frame_right/image_raw raw out:=/camera/right/image_raw
然后再跑数据包
rosbag play --pause 20220216_MCR_normal.bag /imu:=/stim300/imu/data_raw
然后再跑算法
rosrun ORB_SLAM3 Stereo Vocabulary/ORBvoc.txt Examples_old/Stereo/KIN.yaml false
运行示例：
感觉是外参一直搞错了 能跑，但是总是重置 emmm 后面再找找吧 大致探索一下，现在好像知道了 因为自己的数据包一开始录制的时候并不知晓 IMU的初始化需要时间等事宜
roscore
rosrun image_transport republish compressed in:=/stereo/frame_left/image_raw raw out:=/camera/left/image_raw
rosrun image_transport republish compressed in:=/stereo/frame_right/image_raw raw out:=/camera/right/image_raw
然后再跑数据包
rosbag play --pause 20220216_MCR_normal.bag /stim300/imu/data_raw:=/imu
然后再跑算法
rosrun ORB_SLAM3 Stereo_Inertial Vocabulary/ORBvoc.txt Examples_old/Stereo-Inertial/HKUST.yaml false
IMU会重置
if (!mCurrentFrame.mpImuPreintegrated || !mLastFrame.mpImuPreintegrated)
{
cout << "not IMU meas" << endl;
return;
}
可能原因：
Running ORB-SLAM3 with ZED2 always appears error. · Issue #620 · stereolabs/zed-ros-wrapper
The camera/IMU transform is in the standard ROS frame: X Forward, Y left, Z Top.
但是两个相机之间的就没啥问题 所以应该不是
the imu initialization need motion, you can try to move the imu at first till it print the “end VBA2”.
Do you have enough motion in all directions, take a look at using ORBSLAM3 with e.g. EuRoC dataset: MH_01_easy.bag, seems like motion initially is important for the initialization of ORBSLAM3 to work issue
# ORB Extractor: Number o
f features per image
ORBextractor.nFeatures: 1000
# 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: 20
ORBextractor.minThFAST: 7
因为都是室内就没咋调，后面再有兴趣可以看看细节，运行起来感觉还不错，如果想得到pose可以直接pub一下pose，就能当个定位用了
赠人点赞 手有余香 😆；正向回馈 才能更好开放记录 hhh