Hi，your dataset is useful!Thanks!But when I run Vins momo with lab-dynamic,It shows the IMU excitation not enouth!So can not initial.This is my yaml file,can you know the reasons?
%YAML:1.0
#common parameters
imu_topic: "/imu"
image_topic: "/cam0/color"
output_path: "/home/wuyajia/cnn_vins_results/VCU_RVI"
#camera calibration
model_type: PINHOLE
camera_name: camera
image_width: 640
image_height: 480
distortion_parameters:
k1: 0
k2: 0
p1: 0
p2: 0
projection_parameters:
fx: 4.59357e+02
fy: 4.59764e+02
cx: 3.32695e+02
cy: 2.58998e+02
Extrinsic parameter between IMU and Camera.
estimate_extrinsic: 0 # 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.
# 2 Don't know anything about extrinsic parameters. You don't need to give R,T. We will try to calibrate it. Do some rotation movement at beginning.
#If you choose 0 or 1, you should write down the following matrix.
#Rotation from camera frame to imu frame, imu^R_cam
extrinsicRotation: !!opencv-matrix
rows: 3
cols: 3
dt: d
data: [0.00193013, -0.999997, 0.00115338,
-0.999996, -0.0019327, -0.00223606,
0.00223829, -0.00114906, -0.999997]
#Translation from camera frame to imu frame, imu^T_cam
extrinsicTranslation: !!opencv-matrix
rows: 3
cols: 1
dt: d
data: [-0.00817048,0.015075, -0.0110795]
#feature traker paprameters
max_cnt: 150 # 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: 2.0 # ransac threshold (pixel)
show_track: 1 # publish tracking image as topic
equalize: 1 # if image is too dark or light, trun on equalize to find enough features
fisheye: 0 # if using fisheye, trun on it. A circle mask will be loaded to remove edge noisy points
#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: 0.05 # 0.119 #0.1 #0.1 # accelerometer measurement noise standard deviation. #0.2 0.04
gyr_n: 0.01 #0.0208 # 0.01 # gyroscope measurement noise standard deviation. #0.05 0.004
acc_w: 0.001 # 0.000535 #0.001 # accelerometer bias random work noise standard deviation. #0.02
gyr_w: 0.0001 # 0.000089 # 0.0001 # gyroscope bias random work noise standard deviation. #4.0e-5
g_norm: 9.81 # gravity magnitude
#loop closure parameters
loop_closure: 1 # start loop closure
load_previous_pose_graph: 0 # load and reuse previous pose graph; load from 'pose_graph_save_path'
fast_relocalization: 0 # useful in real-time and large project
pose_graph_save_path: "/home/wuyajia/cnn_vins_results/VCU_RVI/pose_graph/" # save and load path
#unsynchronization parameters
estimate_td: 0 # 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)
#rolling shutter parameters
rolling_shutter: 0 # 0: global shutter camera, 1: rolling shutter camera
rolling_shutter_tr: 0.033 # unit: s. rolling shutter read out time per frame (from data sheet).
#visualization parameters
save_image: 1 # save image in pose graph for visualization prupose; you can close this function by setting 0
visualize_imu_forward: 0 # output imu forward propogation to achieve low latency and high frequence results
visualize_camera_size: 0.4 # size of camera marker in RVIZ
![Uploading Screenshot from 2024-05-08 19-56-55.png…]()
Hi，your dataset is useful!Thanks!But when I run Vins momo with lab-dynamic,It shows the IMU excitation not enouth!So can not initial.This is my yaml file,can you know the reasons?
%YAML:1.0
#common parameters
imu_topic: "/imu"
image_topic: "/cam0/color"
output_path: "/home/wuyajia/cnn_vins_results/VCU_RVI"
#camera calibration
model_type: PINHOLE
camera_name: camera
image_width: 640
image_height: 480
distortion_parameters:
k1: 0
k2: 0
p1: 0
p2: 0
projection_parameters:
fx: 4.59357e+02
fy: 4.59764e+02
cx: 3.32695e+02
cy: 2.58998e+02
Extrinsic parameter between IMU and Camera.
estimate_extrinsic: 0 # 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.
# 2 Don't know anything about extrinsic parameters. You don't need to give R,T. We will try to calibrate it. Do some rotation movement at beginning.
#If you choose 0 or 1, you should write down the following matrix.
#Rotation from camera frame to imu frame, imu^R_cam
extrinsicRotation: !!opencv-matrix
rows: 3
cols: 3
dt: d
data: [0.00193013, -0.999997, 0.00115338,
-0.999996, -0.0019327, -0.00223606,
0.00223829, -0.00114906, -0.999997]
#Translation from camera frame to imu frame, imu^T_cam
extrinsicTranslation: !!opencv-matrix
rows: 3
cols: 1
dt: d
data: [-0.00817048,0.015075, -0.0110795]
#feature traker paprameters
max_cnt: 150 # 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: 2.0 # ransac threshold (pixel)
show_track: 1 # publish tracking image as topic
equalize: 1 # if image is too dark or light, trun on equalize to find enough features
fisheye: 0 # if using fisheye, trun on it. A circle mask will be loaded to remove edge noisy points
#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: 0.05 # 0.119 #0.1 #0.1 # accelerometer measurement noise standard deviation. #0.2 0.04
gyr_n: 0.01 #0.0208 # 0.01 # gyroscope measurement noise standard deviation. #0.05 0.004
acc_w: 0.001 # 0.000535 #0.001 # accelerometer bias random work noise standard deviation. #0.02
gyr_w: 0.0001 # 0.000089 # 0.0001 # gyroscope bias random work noise standard deviation. #4.0e-5
g_norm: 9.81 # gravity magnitude
#loop closure parameters
loop_closure: 1 # start loop closure
load_previous_pose_graph: 0 # load and reuse previous pose graph; load from 'pose_graph_save_path'
fast_relocalization: 0 # useful in real-time and large project
pose_graph_save_path: "/home/wuyajia/cnn_vins_results/VCU_RVI/pose_graph/" # save and load path
#unsynchronization parameters
estimate_td: 0 # 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)
#rolling shutter parameters
rolling_shutter: 0 # 0: global shutter camera, 1: rolling shutter camera
rolling_shutter_tr: 0.033 # unit: s. rolling shutter read out time per frame (from data sheet).
#visualization parameters
![Uploading Screenshot from 2024-05-08 19-56-55.png…]()
save_image: 1 # save image in pose graph for visualization prupose; you can close this function by setting 0
visualize_imu_forward: 0 # output imu forward propogation to achieve low latency and high frequence results
visualize_camera_size: 0.4 # size of camera marker in RVIZ