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my_params_camera.yaml
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my_params_camera.yaml
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%YAML:1.0
# Project
project_name: "lvi_sam"
#common parameters
imu_topic: "/handsfree/imu"
image_topic: "/camera/color/image_raw"
point_cloud_topic: "lvi_sam/lidar/deskew/cloud_deskewed"
# Lidar Params
use_lidar: 1 # whether use depth info from lidar or not
lidar_skip: 3 # skip this amount of scans
align_camera_lidar_estimation: 1 # align camera and lidar estimation for visualization
# lidar to camera extrinsic
lidar_to_cam_tx: 0.27255
lidar_to_cam_ty: -0.00053
lidar_to_cam_tz: 0.17954
lidar_to_cam_rx: 0.0
lidar_to_cam_ry: 0.0
lidar_to_cam_rz: 0.0
# extrinsicRPY: !!opencv-matrix
# rows: 3
# cols: 1
# dt: d
# data: [1,0,0,0,1,0,0,0,1]
# camera model
model_type: PINHOLE
camera_name: camera
# Mono camera config
image_width: 640
image_height: 480
distortion_parameters:
k1: 0.148000794688248
k2: -0.217835187249065
p1: 0
p2: 0
projection_parameters:
fx: 617.971050917033
fy: 616.445131524790
cx: 327.710279392468
cy: 253.976983707814
#fisheye_mask: "/config/fisheye_mask_720x540.jpg"
#imu parameters The more accurate parameters you provide, the worse performance
acc_n: 1.2820343288774358e-01 # accelerometer measurement noise standard deviation. #0.2
gyr_n: 2.1309311394972831e-02 # gyroscope measurement noise standard deviation. #0.05
acc_w: 1.3677912958097768e-02 # accelerometer bias random work noise standard deviation. #0.02
gyr_w: 3.6603917782528627e-04 # gyroscope bias random work noise standard deviation. #4.0e-5
g_norm: 9.805 # gravity magnitude
# 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.
#Rotation from camera frame to imu frame, imu^R_cam
extrinsicRotation: !!opencv-matrix
rows: 3
cols: 3
dt: d
data: [ 0.0, 0.0, 1.0,
-1.0, 0.0, 0.0,
0.0, -1.0, 0.0]
#Translation from camera frame to imu frame, imu^T_cam
extrinsicTranslation: !!opencv-matrix
rows: 3
cols: 1
dt: d
data: [0.57711, -0.00012, 0.83333]
#feature traker paprameters
max_cnt: 150 # max feature number in feature tracking
min_dist: 20 # min distance between two features
freq: 20 # 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: 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: 10 # max solver itrations, to guarantee real time
keyframe_parallax: 10.0 # keyframe selection threshold (pixel)
#unsynchronization parameters
estimate_td: 0 # online estimate time offset between camera and imu
td: 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 # unit: s. rolling shutter read out time per frame (from data sheet).
#loop closure parameters
loop_closure: 1 # start loop closure
skip_time: 0.0
skip_dist: 0.0
debug_image: 0 # save raw image in loop detector for visualization prupose; you can close this function by setting 0
match_image_scale: 0.5
vocabulary_file: "/config/brief_k10L6.bin"
brief_pattern_file: "/config/brief_pattern.yml"