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你好!感谢你优秀的开源代码,我在运行NTU Viral dataset的时候,发现无法正常建图,会一直往下掉。有尝试将gravity设为+9.8也无法改善。用原本您的开源code(还是ikd-tree版本的)在相同的config下却可以正常建图,不知道是什么情况?
以下是我的config文件:
common:
lid_topic: "/os1_cloud_node1/points"
imu_topic: "/imu/imu"
con_frame: false # true: if you need to combine several LiDAR frames into one
con_frame_num: 1 # the number of frames combined
cut_frame: false # true: if you need to cut one LiDAR frame into several subframes
cut_frame_time_interval: 0.1 # should be integral fraction of 1 / LiDAR frequency
time_diff_lidar_to_imu: 0.0 # Time offset between LiDAR and IMU calibrated by other algorithms, e.g., LI-Init (find in Readme)
preprocess:
lidar_type: 3
scan_line: 16
timestamp_unit: 3 # the unit of time/t field in the PointCloud2 rostopic: 0-second, 1-milisecond, 2-microsecond, 3-nanosecond.
blind: 4.0
mapping:
imu_en: true
extrinsic_est_en: false # for aggressive motion, set this variable false
imu_time_inte: 0.01 # = 1 / frequency of IMU
lidar_time_inte: 0.1
satu_acc: 30.0 # the saturation value of IMU's acceleration. not related to the units
satu_gyro: 35 # the saturation value of IMU's angular velocity. not related to the units
acc_norm: 9.81 # 1.0 for g as unit, 9.81 for m/s^2 as unit of the IMU's acceleration
lidar_meas_cov: 0.01 # 0.001
acc_cov_output: 500
gyr_cov_output: 1000
b_acc_cov: 0.0001
b_gyr_cov: 0.0001
imu_meas_acc_cov: 0.1 #0.1 # 2
imu_meas_omg_cov: 0.1 #0.1 # 2
gyr_cov_input: 0.01 # for IMU as input model
acc_cov_input: 0.1 # for IMU as input model
plane_thr: 0.1 # 0.05, the threshold for plane criteria, the smaller, the flatter a plane
match_s: 81
ivox_grid_resolution: 2.0
gravity: [0.0, 0.0, -9.810] # [-0.30, 0.880, -9.76] # liosam [0.0, 9.810, 0.0] # # preknown gravity, use when imu_en is false or start from a non-stationary state
gravity_init: [0.0, 0.0, -9.810] # preknown gravity in the initial IMU frame for unstationary start or in the initial LiDAR frame for using without IMU
extrinsic_T: [ 0.05, 0.0, -0.055] # ulhk # [-0.5, 1.4, 1.5] # utbm
# extrinsic_R: [ 0, 1, 0,
# -1, 0, 0,
# 0, 0, 1 ] # ulhk 5 6
# extrinsic_R: [ 0, -1, 0,
# 1, 0, 0,
# 0, 0, 1 ] # utbm 1, 2
extrinsic_R: [ 1, 0, 0,
0, 1, 0,
0, 0, 1 ] # ulhk 4 utbm 3
publish:
path_en: true # false: close the path output
scan_publish_en: true # false: close all the point cloud output
scan_bodyframe_pub_en: false # true: output the point cloud scans in IMU-body-frame
pcd_save:
pcd_save_en: false
interval: -1 # how many LiDAR frames saved in each pcd file;
# -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames.
The text was updated successfully, but these errors were encountered:
你好!感谢你优秀的开源代码,我在运行NTU Viral dataset的时候,发现无法正常建图,会一直往下掉。有尝试将gravity设为+9.8也无法改善。用原本您的开源code(还是ikd-tree版本的)在相同的config下却可以正常建图,不知道是什么情况?
以下是我的config文件:
common:
lid_topic: "/os1_cloud_node1/points"
imu_topic: "/imu/imu"
con_frame: false # true: if you need to combine several LiDAR frames into one
con_frame_num: 1 # the number of frames combined
cut_frame: false # true: if you need to cut one LiDAR frame into several subframes
cut_frame_time_interval: 0.1 # should be integral fraction of 1 / LiDAR frequency
time_diff_lidar_to_imu: 0.0 # Time offset between LiDAR and IMU calibrated by other algorithms, e.g., LI-Init (find in Readme)
preprocess:
lidar_type: 3
scan_line: 16
timestamp_unit: 3 # the unit of time/t field in the PointCloud2 rostopic: 0-second, 1-milisecond, 2-microsecond, 3-nanosecond.
blind: 4.0
mapping:
imu_en: true
extrinsic_est_en: false # for aggressive motion, set this variable false
imu_time_inte: 0.01 # = 1 / frequency of IMU
lidar_time_inte: 0.1
satu_acc: 30.0 # the saturation value of IMU's acceleration. not related to the units
satu_gyro: 35 # the saturation value of IMU's angular velocity. not related to the units
acc_norm: 9.81 # 1.0 for g as unit, 9.81 for m/s^2 as unit of the IMU's acceleration
lidar_meas_cov: 0.01 # 0.001
acc_cov_output: 500
gyr_cov_output: 1000
b_acc_cov: 0.0001
b_gyr_cov: 0.0001
imu_meas_acc_cov: 0.1 #0.1 # 2
imu_meas_omg_cov: 0.1 #0.1 # 2
gyr_cov_input: 0.01 # for IMU as input model
acc_cov_input: 0.1 # for IMU as input model
plane_thr: 0.1 # 0.05, the threshold for plane criteria, the smaller, the flatter a plane
match_s: 81
ivox_grid_resolution: 2.0
gravity: [0.0, 0.0, -9.810] # [-0.30, 0.880, -9.76] # liosam [0.0, 9.810, 0.0] # # preknown gravity, use when imu_en is false or start from a non-stationary state
gravity_init: [0.0, 0.0, -9.810] # preknown gravity in the initial IMU frame for unstationary start or in the initial LiDAR frame for using without IMU
extrinsic_T: [ 0.05, 0.0, -0.055] # ulhk # [-0.5, 1.4, 1.5] # utbm
# extrinsic_R: [ 0, 1, 0,
# -1, 0, 0,
# 0, 0, 1 ] # ulhk 5 6
# extrinsic_R: [ 0, -1, 0,
# 1, 0, 0,
# 0, 0, 1 ] # utbm 1, 2
extrinsic_R: [ 1, 0, 0,
0, 1, 0,
0, 0, 1 ] # ulhk 4 utbm 3
odometry:
publish_odometry_without_downsample: false
publish:
path_en: true # false: close the path output
scan_publish_en: true # false: close all the point cloud output
scan_bodyframe_pub_en: false # true: output the point cloud scans in IMU-body-frame
pcd_save:
pcd_save_en: false
interval: -1 # how many LiDAR frames saved in each pcd file;
# -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames.
The text was updated successfully, but these errors were encountered: