forked from xtdrone/XTDrone
67 lines
2.5 KiB
YAML
67 lines
2.5 KiB
YAML
%YAML:1.0
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#common parameters
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imu: 1
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num_of_cam: 2 # 1 or 2
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imu_topic: "/data_generator/imu"
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image0_topic: "/cam0/image_raw"
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image1_topic: "/cam1/image_raw"
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output_path: "/home/tony-ws1/output/"
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cam0_calib: "cam0_mei.yaml"
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cam1_calib: "cam1_mei.yaml"
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image_width: 600
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image_height: 600
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# Extrinsic parameter between IMU and Camera.
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estimate_extrinsic: 0 # 0 Have an accurate extrinsic parameters. We will trust the following imu^R_cam, imu^T_cam, don't change it.
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# 1 Have an initial guess about extrinsic parameters. We will optimize around your initial guess.
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# 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.
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#If you choose 0 or 1, you should write down the following matrix.
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#cam0 to body
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body_T_cam0: !!opencv-matrix
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rows: 4
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cols: 4
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dt: d
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data: [0, 0, -1, -0.02,
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-1, 0, 0, 0,
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0, 1, 0, 0.02,
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0, 0, 0, 1]
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#cam1 to body
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body_T_cam1: !!opencv-matrix
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rows: 4
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cols: 4
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dt: d
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data: [0, 0, -1, 0,
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-1, 0, 0, 0,
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0, 1, 0, 0.3,
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0, 0, 0, 1]
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#Multiple thread support
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multiple_thread: 0
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#optimization parameters
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max_solver_time: 0.04 # max solver itration time (ms), to guarantee real time
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max_num_iterations: 8 # max solver itrations, to guarantee real time
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keyframe_parallax: 10.0 # keyframe selection threshold (pixel)
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#imu parameters The more accurate parameters you provide, the better performance
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acc_n: 0.2 # accelerometer measurement noise standard deviation. #0.2
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gyr_n: 0.02 # gyroscope measurement noise standard deviation. #0.05
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acc_w: 0.0002 # accelerometer bias random work noise standard deviation. #0.02
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gyr_w: 2.0e-5 # gyroscope bias random work noise standard deviation. #4.0e-5
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g_norm: 9.805 # gravity magnitude
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#unsynchronization parameters
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estimate_td: 0 # online estimate time offset between camera and imu
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td: 0.0 # initial value of time offset. unit: s. readed image clock + td = real image clock (IMU clock)
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#visualization parameters
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save_image: 0 # save image in pose graph for visualization prupose; you can close this function by setting 0
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visualize_imu_forward: 1 # output imu forward propogation to achieve low latency and high frequence results
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visualize_camera_size: 0.4 # size of camera marker in RVIZ
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