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- %YAML:1.0
- # Project
- project_name: "lvi_sam"
- #common parameters
- imu_topic: "/handsfree/imu"
- image_topic: "/camera/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: 0.00
- lidar_to_cam_ty: -0.00
- lidar_to_cam_tz: -0.00
- lidar_to_cam_rx: 0.0
- lidar_to_cam_ry: 0.0
- lidar_to_cam_rz: 0.035
- # camera model
- model_type: PINHOLE
- camera_name: camera
- # Mono camera config
- image_width: 1280
- image_height: 720
- distortion_parameters:
- k1: -0.3013191223144531
- k2: 0.08228302001953125
- p1: 0.00012969970703125
- p2: -0.000377655029296875
- projection_parameters:
- fx: 711.79833984375
- fy: 712.151123046875
- cx: 629.6566772460938
- cy: 368.3511047363281
- #imu parameters The more accurate parameters you provide, the worse performance
- acc_n: 1.6736505844052338e-02 # accelerometer measurement noise standard deviation.
- gyr_n: 1.5727292142586351e-01 # gyroscope measurement noise standard deviation.
- acc_w: 7.0168288079436173e-03 # accelerometer bias random work noise standard deviation.
- gyr_w: 5.9547994868495480e-02 # gyroscope bias random work noise standard deviation.
- g_norm: 9.805 #
- # 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, 1,
- -1, 0, 0,
- 0, -1, 0]
- #Translation from camera frame to imu frame, imu^T_cam
- extrinsicTranslation: !!opencv-matrix
- rows: 3
- cols: 1
- dt: d
- data: [0.006422381632411965, 0.019939800449065116, 0.03364235163589248]
- #feature traker paprameters
- max_cnt: 150 # max feature number in feature tracking
- min_dist: 20 # 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
- 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: 0.035 # 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"
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