5.4. ubuntu 18.04 OAK-D系列相机 运行VINS-Fusion 双目+IMU

参考:https://github.com/HKUST-Aerial-Robotics/VINS-Fusion

5.4.1. 准备

Note

依赖包括ROS、depthai_ros、Ceres Solver,在下面的标定教程中

相机标定参数,参考 标定教程

三个配置文件,按照标定参数填写,填写时注意注释,这里是 OAK-D-S2的校准参数以及vins-fusion的配置文件

三个文件放到同级目录下,可以新建目录OAK-D放到dai_ws/src/VINS-Fusion/config

config.yaml

%YAML:1.0

#common parameters
#support: 1 imu 1 cam; 1 imu 2 cam: 2 cam;
imu: 1
num_of_cam: 2

imu_topic: "/stereo_inertial_publisher/imu"
image0_topic: "/stereo_inertial_publisher/left/image_rect"
image1_topic: "/stereo_inertial_publisher/right/image_rect"

cam0_calib: "left.yaml"
cam1_calib: "right.yaml"
image_width: 640
image_height: 400


# Extrinsic parameter between IMU and Camera.
estimate_extrinsic: 1   # 0  Have an accurate extrinsic parameters. We will trust the following imu^R_cam, imu^T_cam, don't change it.
                        # 1  Have an initial guess about extrinsic parameters. We will optimize around your initial guess.

body_T_cam0: !!opencv-matrix # Inverse of Kalibr result, (transpose for rotation matrix, T'=-R'T)
rows: 4
cols: 4
dt: d
data: [ 0.00128876,  0.99999914, -0.00023326,  -0.00323207,
        0.99999859,  -0.00128851, 0.00107949, -0.06857642,
        0.00107919, -0.00023466, -0.99999939, -0.00014712,
        0, 0, 0, 1]

#T_cn_cnm1: #T_c1_c0 : c0's points from c1's view

body_T_cam1: !!opencv-matrix
rows: 4
cols: 4
dt: d
data: [ 0.00138612,   0.99999838, 0.00115110, -0.00301687,
        0.99998778,  -0.00139157, 0.00474275,  0.00675044,
        0.00474435, 0.00114451, -0.99998809, -0.00109913,
        0, 0, 0, 1]

#Multiple thread support
multiple_thread: 1

#feature traker paprameters
max_cnt: 130            # max feature number in feature tracking
min_dist: 30            # min distance between two features
freq: 0                 # frequence (Hz) of publish tracking result. At least 10Hz for good estimation. If set 0, the frequence will be same as raw image
F_threshold: 1.0        # ransac threshold (pixel)
show_track: 1           # publish tracking image as topic
flow_back: 1            # perform forward and backward optical flow to improve feature tracking accuracy

#optimization parameters
max_solver_time: 0.04  # max solver itration time (ms), to guarantee real time
max_num_iterations: 8   # max solver itrations, to guarantee real time
keyframe_parallax: 10.0 # keyframe selection threshold (pixel)

#imu parameters       The more accurate parameters you provide, the better performance
acc_n: 0.1          # accelerometer measurement noise standard deviation.
gyr_n: 0.01         # gyroscope measurement noise standard deviation.
acc_w: 0.001        # accelerometer bias random work noise standard deviation.
gyr_w: 0.0001       # gyroscope bias random work noise standard deviation.
g_norm: 9.81007     # gravity magnitude

#unsynchronization parameters
estimate_td: 1                      # online estimate time offset between camera and imu
td: 0.0                         # initial value of time offset. unit: s. readed image clock + td = real image clock (IMU clock)

#loop closure parameters
load_previous_pose_graph: 0        # load and reuse previous pose graph; load from 'pose_graph_save_path'
pose_graph_save_path: "~/output/pose_graph/" # save and load path
save_image: 1                   # save image in pose graph for visualization prupose; you can close this function by setting 0

left.yaml

%YAML:1.0
---
model_type: PINHOLE
camera_name: camera
image_width: 640
image_height: 400
distortion_parameters:
k1: 0.011099142353676499
k2: -0.05769482092275897
p1: -0.0009757653113701839
p2: 0.0025548857914714745
projection_parameters:
fx: 401.8064
fy: 400.5184
cx: 323.9370
cy: 193.8434

right.yaml

%YAML:1.0
---
model_type: PINHOLE
camera_name: camera
image_width: 640
image_height: 400
distortion_parameters:
k1: 0.005536021298200547
k2: -0.048229113205249675
p1: -0.0002985290327403832
p2: 0.0037187807087799125
projection_parameters:
fx: 401.9601
fy: 399.9079
cx: 325.5761
cy: 194.2576

5.4.2. 构建VINS-Fusion

cd ~/dai_ws/src
git clone https://github.com/HKUST-Aerial-Robotics/VINS-Fusion.git
cd ..
catkin_make
source ~/dai_ws/devel/setup.bash

Note

如果此步骤失败,请尝试寻找另一台系统干净的计算机或重新安装 Ubuntu 和 ROS

5.4.3. 运行示例

这里演示的是双目+imu,其他示例参考教程开头的github原作者教程

分别打开一个终端运行每一行命令,注意环境要激活

roslaunch depthai_examples stereo_inertial_node.launch enableRviz:=false depth_aligned:=false
roslaunch vins vins_rviz.launch
rosrun vins vins_node ~/dai_ws/src/VINS-Fusion/config/oak_d_s2/config.yaml
(optional) rosrun loop_fusion loop_fusion_node ~/dai_ws/src/VINS-Fusion/config/oak_d_s2/config.yaml
../../_images/vins_fusion_res.png

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