一、Kalibr 软件运行
- Kalibr 制作采集的图片和IMU数据生成 .bag 文件 （坑最全，解决方法最简单，最详细哈哈）_努力努力努力-CSDN博客
- Kalibr 标定Camera 和 IMU 参数（自己采集的数据)_努力努力努力-CSDN博客
说明：时间有限，我先去干其他的事情，本博客后面会详细写
标定的结果
435i_imu_param.yaml
%YAML:1.0
---
type: IMU
name: d435i
Gyr:
unit: " rad/s"
avg-axis: #取平均值作为标定结果
gyr_n: 2.1732068912927271e-03 #白噪声
gyr_w: 1.7797900203083191e-05 #偏置
x-axis:
gyr_n: 2.1391701666780305e-03
gyr_w: 1.9895954849984289e-05
y-axis:
gyr_n: 2.5929821451625827e-03
gyr_w: 1.9897115334756576e-05
z-axis:
gyr_n: 1.7874683620375685e-03
gyr_w: 1.3600630424508712e-05
Acc:
unit: " m/s^2"
avg-axis: #取平均值作为标定结果
acc_n: 2.3786845794688424e-02 #白噪声
acc_w: 5.9166889270489845e-04 #偏置
x-axis:
acc_n: 2.1534587157119929e-02
acc_w: 6.2088900106164286e-04
y-axis:
acc_n: 2.2044805094743814e-02
acc_w: 6.2033852597901415e-04
z-axis:
acc_n: 2.7781145132201524e-02
acc_w: 5.3377915107403855e-04
- Camera-system parameters
Calibration results
====================
Camera-system parameters:
cam0 (/infra_left):
type: <class 'aslam_cv.libaslam_cv_python.EquidistantDistortedPinholeCameraGeometry'>
distortion: [ 0.40059551 -0.73828444 4.12512287 -6.18517722] +- [ 0.0096611 0.06314745 0.09801433 0.08414741]
projection: [ 386.71250573 387.18240561 320.55905537 239.8204248 ] +- [ 0.14888357 0.16540833 0.34725177 0.27395728]
reprojection error: [-0.000012, -0.000007] +- [0.148506, 0.106890]
cam1 (/infra_right):
type: <class 'aslam_cv.libaslam_cv_python.EquidistantDistortedPinholeCameraGeometry'>
distortion: [ 0.36572062 -0.38257775 3.28883181 -6.72070632] +- [ 0.01450564 0.12847927 0.29582794 0.15893508]
projection: [ 386.94988374 387.35006645 321.07250734 240.44316048] +- [ 0.13223764 0.16242541 0.3057414 0.26940346]
reprojection error: [0.000004, -0.000005] +- [0.164897, 0.108821]
cam2 (/color):
type: <class 'aslam_cv.libaslam_cv_python.EquidistantDistortedPinholeCameraGeometry'>
distortion: [ 0.24904226 2.8043341 -15.57145308 26.7679881 ] +- [ 0.0099252 0.09318402 0.2472954 0.11722548]
projection: [ 611.67156667 611.71826379 323.28627384 240.57596379] +- [ 0.10541019 0.13846652 0.29617104 0.23250236]
reprojection error: [0.000003, -0.000001] +- [0.207269, 0.167030]
baseline T_1_0:
q: [-0.00053684 0.00066745 0.00003144 0.99999963] +- [ 0.00122298 0.00096443 0.00015755]
t: [ 0.04996444 0.00001392 0.0000511 ] +- [ 0.00013475 0.00012981 0.00032963]
baseline T_2_1:
q: [ 0.00164081 -0.00396511 0.00180494 0.99998916] +- [ 0.00097517 0.00060299 0.00012675]
t: [ 0.01598901 -0.00016613 0.00066788] +- [ 0.00010335 0.00010625 0.00027807]
Target configuration
====================
Type: aprilgrid
Tags:
Rows: 6
Cols: 6
Size: 0.021 [m]
Spacing 0.00614285721 [m]
-
IMU+相机联合标定
Calibration results
===================
Normalized Residuals
----------------------------
Reprojection error (cam0): mean 0.309357318376, median 0.251843164322, std: 0.230997537619
Gyroscope error (imu0): mean 0.265159164537, median 0.228561532289, std: 0.176229173919
Accelerometer error (imu0): mean 0.221421363455, median 0.195499299021, std: 0.128105231257
Residuals
----------------------------
Reprojection error (cam0) [px]: mean 0.309357318376, median 0.251843164322, std: 0.230997537619
Gyroscope error (imu0) [rad/s]: mean 0.00814934517662, median 0.00702456135722, std: 0.00541618982304
Accelerometer error (imu0) [m/s^2]: mean 0.0744854379606, median 0.065765338454, std: 0.0430941897671
Transformation (cam0):
-----------------------
T_ci: (imu0 to cam0):
[[ 0.99996271 -0.00181009 0.00844446 0.02651246]
[ 0.00171691 0.9999377 0.01102943 0.00830124]
[-0.0084639 -0.01101452 0.99990352 -0.01286511]
[ 0. 0. 0. 1. ]]
T_ic: (cam0 to imu0):
[[ 0.99996271 0.00171691 -0.0084639 -0.02663461]
[-0.00181009 0.9999377 -0.01101452 -0.00839444]
[ 0.00844446 0.01102943 0.99990352 0.01254843]
[ 0. 0. 0. 1. ]]
timeshift cam0 to imu0: [s] (t_imu = t_cam + shift)
-0.0253737394755
Gravity vector in target coords: [m/s^2]
[ 0.25486723 -9.78147004 -0.65292373]
Calibration configuration
=========================
cam0
-----
Camera model: pinhole
Focal length: [611.6715666677603, 611.7182637869023]
Principal point: [323.2862738374678, 240.57596378988336]
Distortion model: equidistant
Distortion coefficients: [0.24904225693789128, 2.804334100694488, -15.571453079619436, 26.767988102446466]
Type: aprilgrid
Tags:
Rows: 6
Cols: 6
Size: 0.021 [m]
Spacing 0.00614285721 [m]
IMU configuration
=================
IMU0:
----------------------------
Model: calibrated
Update rate: 200.0
Accelerometer:
Noise density: 0.0237868457947
Noise density (discrete): 0.336396799289
Random walk: 0.000591668892705
Gyroscope:
Noise density: 0.00217320689129
Noise density (discrete): 0.0307337865951
Random walk: 1.77979002031e-05
T_i_b
[[ 1. 0. 0. 0.]
[ 0. 1. 0. 0.]
[ 0. 0. 1. 0.]
[ 0. 0. 0. 1.]]
time offset with respect to IMU0: 0.0 [s]
VINS Yaml文件配置
%YAML:1.0
#common parameters
imu_topic: "/camera/imu"
image_topic: "/camera/color/image_raw"
output_path: "/home/fb/output/"
#camera calibration
model_type: PINHOLE
camera_name: camera
image_width: 640
image_height: 480
#TODO modify distortion
distortion_parameters:
k1: 0.10364469
k2: -0.1823355
p1: 0.002330617
p2: 0.0037446
projection_parameters:
fx: 601.226091
fy: 601.3432164
cx: 332.171979
cy: 240.5101526
# 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: [ 0.99977841, 0.00412757, 0.02064201,
# -0.00374241, 0.99981883, -0.01866291,
# -0.0207153, 0.01858152, 0.99961273 ]
data: [ 0.99998318, -0.00302186, -0.00495021,
0.00296347, 0.99992645, -0.01176032,
0.00498539, 0.01174545, 0.99991859 ]
#Translation from camera frame to imu frame, imu^T_cam
extrinsicTranslation: !!opencv-matrix
rows: 3
cols: 1
dt: d
# data: [-0.02154582, 0.00681016, 0.02740755]
data: [-0.0132516, -0.00082214, 0.01535377]
#feature traker paprameters
max_cnt: 200 # max feature number in feature tracking
min_dist: 15 # 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
equalize: 0 # 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
#for handheld, wheeld
acc_n: 0.021793 # accelerometer measurement noise standard deviation. #0.2
gyr_n: 0.00215568 # gyroscope measurement noise standard deviation. #0.05
acc_w: 0.00050207 # accelerometer bias random work noise standard deviation. #0.02
gyr_w: 1.71656e-05 # gyroscope bias random work noise standard deviation. #4.0e-5
#for tracked applications
#acc_n: 0.5 # accelerometer measurement noise standard deviation. #0.2
#gyr_n: 0.01 # gyroscope measurement noise standard deviation. #0.05
#acc_w: 0.001 # accelerometer bias random work noise standard deviation. #0.02
#gyr_w: 2.0e-5 # gyroscope bias random work noise standard deviation. #4.0e-5
g_norm: 9.805 # gravity magnitude
#loop closure parameters
loop_closure: 1 # start loop closure
fast_relocalization: 1 # useful in real-time and large project
load_previous_pose_graph: 0 # load and reuse previous pose graph; load from 'pose_graph_save_path'
#pose_graph_save_path: "/home/fb/output/pose_graph/" # save and load path
pose_graph_save_path: "*****"
#unsynchronization parameters
estimate_td: 1 # online estimate time offset between camera and imu
td: -0.0237 # initial value of time offset. unit: s. readed image clock + td = real image clock (IMU clock)
#rolling shutter parameters
rolling_shutter: 1 # 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
参考：