- Edited
Hello everyone,
I manged to run and install and run Depth ai & Vins-Fusion to run on Oak-D pro W camera but when the code starts every thing seems to be fine but once I start moving the camera the Tf starts to get crazy and drift very far.
I need some help on what I need to configure or edit.
This is my config file:
%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"
output_path: "/home/pi/Documents"
cam0_calib: "left.yaml"
cam1_calib: "right.yaml"
image_width: 1280
image_height: 800
# Extrinsic parameter between IMU and Camera.
estimate_extrinsic: 1 # 0 Have an accurate extrinsic parameters. We will trust the following imuR_cam, imuT_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
rows: 4
cols: 4
dt: d
data: [ 0, 0, 1, 0,
-1, 0, 0, 0.0375,
0, -1, 0, 0,
0, 0, 0, 1 ]
body_T_cam1: !!opencv-matrix
rows: 4
cols: 4
dt: d
data: [ 0, 0, 1, 0,
-1, 0, 0, -0.0375,
0, -1, 0, 0,
0, 0, 0, 1 ]
#Multiple thread support
multiple_thread: 0
#feature traker paprameters
max_cnt: 150 # max feature number in feature tracking
min_dist: 30 # min distance between two features
freq: 10 # 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. #0.2 0.04
gyr_n: 0.01 # gyroscope measurement noise standard deviation. #0.05 0.004
acc_w: 0.001 # accelerometer bias random work noise standard deviation. #0.002
gyr_w: 0.0001 # gyroscope bias random work noise standard deviation. #4.0e-5
g_norm: 9.805 # gravity magnitude
#unsynchronization parameters
estimate_td: 1 # online estimate time offset between camera and imu
td: 0.00 # 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: "/home/pi/Documents" # save and load path
save_image: 0 # save image in pose graph for visualization prupose; you can close this function by setting 0
```
The Left eye config:
%YAML:1.0
---
model_type: PINHOLE
camera_name: camera
image_width: 1280
image_height: 800
distortion_parameters:
k1: 0.0
k2: 0.0
p1: 0.0
p2: 0.0
projection_parameters:
fx: 832.2688598632812
fy: 835.7731323242188
cx: 625.9611206054688
cy: 354.8282775878906
The Right eye config:
%YAML:1.0
---
model_type: PINHOLE
camera_name: camera
image_width: 1280
image_height: 800
distortion_parameters:
k1: 0.0
k2: 0.0
p1: 0.0
p2: 0.0
projection_parameters:
fx: 841.0867309570312
fy: 844.6397705078125
cx: 617.4251708984375
cy: 354.2491760253906
Thank you.
