Comments (13)
你可以确认一下平面优化是否开启了
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我直接使用的是原始的参数如下,请问还需要在哪里开启呢?
imu: 1
wheel: 1
only_initial_with_wheel: 0 #只利用wheel进行初始化,不加入因子图
plane: 1
num_of_cam: 2
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我确认已经开启平面优化了
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你可以尝试一下加大平面约束的权重
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无论怎么调整平面约束的权重,都无法使得轨迹回到水平位置
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我发现不管开不开平面约束,出来的轨迹都是差不多的。平面约束好像用处不大?
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你说的这个现象跟我之前的实验结论不太一致,你用的是哪个配置文件呢
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使用这个realsense_stereo_imu_config_ridgeback.yaml:
%YAML:1.0
#common parameters
#support: 1 imu 1 cam; 1 imu 2 cam: 2 cam;
imu: 1
wheel: 1
only_initial_with_wheel: 0 #只利用wheel进行初始化,不加入因子图
plane: 1
num_of_cam: 2
imu_topic: "/imu/data"
wheel_topic: "/ridgeback_velocity_controller/odom" #"/ridgeback_velocity_controller/odom", “/odometry/filtered”
#TODO check the distortion
image0_topic: "/camera/infra1/image_rect_raw"
image1_topic: "/camera/infra2/image_rect_raw"
output_path: "/home/td/slam/vins_fusion_ws/src/VINS-Fusion/output"
cam0_calib: "infra1.yaml"
cam1_calib: "infra2.yaml"
image_width: 640
image_height: 480
# Extrinsic parameter between IMU and Camera.
estimate_extrinsic: 1 # 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.
extrinsic_type: 3 # 0 ALL
# 1 Only translation
# 2 Only Rotation
# 3 no z
# 4 no rotation and no z
body_T_cam0: !!opencv-matrix
rows: 4
cols: 4
dt: d
data: [ 0.0, 0.0, 1.0, 0.041,
-1.0, 0.0, 0.0, 0.307,
0.0, -1.0, 0.0, 0.544,
0, 0, 0, 1 ]
body_T_cam1: !!opencv-matrix
rows: 4
cols: 4
dt: d
data: [ 0.0, 0.0, 1.0, 0.041,
-1.0, 0.0, 0.0, 0.258,
0.0, -1.0, 0.0, 0.544,
0, 0, 0, 1 ]
# Extrinsic parameter between IMU and Wheel.
estimate_wheel_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.
extrinsic_type_wheel: 3 # 0 ALL
# 1 Only translation
# 2 Only Rotation
# 3 no z
# 4 no rotation and no z
#wheel to body
body_T_wheel: !!opencv-matrix
rows: 4
cols: 4
dt: d
data: [1, 0, 0, -0.208,
0, 1, 0, 0.290,
0, 0, 1, -0.168,
0, 0, 0, 1]
#plane noise
#mono:0.01 stereo:0.005
roll_n: 0.01
#mono:0.01 stereo:0.005
pitch_n: 0.01
#mono:0.05 stereo:0.025
zpw_n: 0.05
#Multiple thread support
multiple_thread: 1
#feature traker paprameters
max_cnt: 150 # max feature number in feature tracking
min_dist: 30 # 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
flow_back: 1 # perform forward and backward optical flow to improve feature tracking accuracy
#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: 0.1 # accelerometer measurement noise standard deviation. #0.2 0.04
gyr_n: 0.05 # gyroscope measurement noise standard deviation. #0.05 0.004
acc_w: 7.1765713730075628e-04 # accelerometer bias random work noise standard deviation. #0.002
gyr_w: 4.0e-05 # gyroscope bias random work noise standard deviation. #4.0e-5
g_norm: 9.805 # gravity magnitude
#wheel parameters
# rad/s mono:0.004 stereo:0.002
wheel_gyro_noise_sigma: 0.004
# m/s mono:0.01 stereo:0.006
wheel_velocity_noise_sigma: 0.01
estimate_wheel_intrinsic: 0
# 0 Have an accurate intrinsic parameters. We will trust the following sx, sy, sw, don't change it.
# 1 Have an initial guess about intrinsic parameters. We will optimize around your initial guess.
# 2 TODO Don't know anything about intrinsic parameters. You don't need to give sx, sy, sw. We will try to calibrate it. Do some rotation movement at beginning.
#If you choose 0 or 1, you should write down the following sx, sy, sw.
# wheel intrinsic
sx: 1.0
sy: 1.0
sw: 1.0
#unsynchronization parameters
estimate_td: 0 # online estimate time offset between camera and imu
td: 0.00 # initial value of time offset. unit: s. readed image clock + td = real image clock (IMU clock)
#unsynchronization parameters
estimate_td_wheel: 0 # online estimate time offset between camera and wheel
td_wheel: 0.0 # initial value of time offset. unit: s. readed image clock + td = real image clock (IMU clock)
#loop closure parameters
load_previous_pose_graph: 0 # load and reuse previous pose graph; load from 'pose_graph_save_path'
pose_graph_save_path: "/home/td/slam/vins_fusion_ws/src/VINS-Fusion/output/pose_graph" # save and load path
save_image: 0 # save image in pose graph for visualization prupose; you can close this function by setting 0
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@ChenHuang20 可以试下把平面约束关了,对比下结果
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开和关基本没有区别
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@ChenHuang20 可以对比下单目情况下的平面约束开关
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