i2Nav-WHU
Help testing code
Hi, I hardly try to get result with your algorithm but failed... I dont have lidar. your code successfully run to end of my bag file but all features are classified to outlier! trajectory only show line!!! I change nearest/far depth and reprojection error and .... I think reprojection error and depth scale is problem. my imu installed frd and my camera is install in a manner to see down.
could you plz help me with this.
Best Regards.
my sensor config from kalibr: distortion: [-0.09830587 0.12908673 0.00000912 0.00030652] +- [ 0.00149047 0.00370425 0.00017228 0.0002321 ] projection: [ 1051.87491107 1049.92208525 664.78113662 375.40768068] +- [ 0.88491547 0.84498195 0.83076271 0.66532587] reprojection error: [0.000000, 0.000000] +- [0.212658, 0.206313] Transformation (cam0):
T_ci: (imu0 to cam0): [[-0.02743445 0.99960638 -0.00586749 0.00271632] [-0.9996219 -0.02744476 -0.00168346 -0.05918756] [-0.00184383 0.00581909 0.99998137 -0.01591447] [ 0. 0. 0. 1. ]] T_ic: (cam0 to imu0): [[-0.02743445 -0.9996219 -0.00184383 -0.05912 ] [ 0.99960638 -0.02744476 0.00581909 -0.00424703] [-0.00586749 -0.00168346 0.99998137 0.01583048] [ 0. 0. 0. 1. ]] timeshift cam0 to imu0: [s] (t_imu = t_cam + shift) -0.0160764751881
###################### yaml file #######################################################
Configuration file for LE-VINS
结果输出路径
outputpath: "/data/LE-VINS/workspace/output" is_make_outputdir: true
是否开启可视化
is_use_visualization: true
ROS接口配置
ros: imu_topic: "/imu0" image_topic: "/cam0" lidar_topic: "" use_compressed_image: false # 使用压缩图像
# 读取ROS包
is_read_bag: true
bag_file: ""
IMU噪声建模参数
imu: imudatarate: 204 # IMU原始数据频率, Hz is_use_zupt: true # 使用零速检测和零速约束
arw: 0.2 # deg/sqrt(hr)
vrw: 0.5 # m/s/sqrt(hr)
gbstd: 10.0 # deg/hr
abstd: 150.0 # mGal
corrtime: 1.0 # hr
gb_prior_std: 1500 # deg/hr
ab_prior_std: 10000 # mGal
优化器
optimizer: optimize_reprojection_std: 15 # 像素重投影误差 optimize_point_to_plane_std: 0.1 # 点到面观测协方差, m optimize_window_size: 10 # 滑动窗口大小
optimize_estimate_cam_extrinsic: true # 是否估计相机和IMU的外参
optimize_estimate_cam_td: true # 是否估计相机和IMU之间的时间间隔
optimize_cam_extrinsic_accurate: true # 相机和IMU的外参初值准确
visual: is_use_lidar_depth: false # 使用激光深度增强 track_max_features: 200 # 最大提取特征数量
# 内参 [fx, fy, cx, cy(, skew)]
# intrinsic:
# [
# 8.1640221474060002e+02,
# 8.1738388562809996e+02,
# 6.0882658427579997e+02,
# 2.6668865652440002e+02,
# -2.3882017757999998e+00,
# ]
# # 畸变参数 [k1, k2, p1, p2(, k3)]
# distortion:
# [
# -5.0040485799999999e-02,
# 1.2001217170000000e-01,
# -6.2598060000000004e-04,
# -1.1825064000000000e-03,
# -6.3505207999999994e-02,
# ]
intrinsic: [1051.87491107, 1049.92208525, 664.78113662, 375.40768068]
# 畸变参数 [k1, k2, p1, p2(, k3)]
# Distortion parameters
distortion: [-0.09830587, 0.12908673, 0.00000912, 0.00030652]
# 图像分辨率
resolution: [1280, 720]
# 相机IMU外参 Pb = q_b_c * Pc + t_b_c
# q (x, y, z, w)
q_b_c: [-0.707116, -0.000057, 0.000454, 0.707097]
t_b_c: [0, -.05, 0]
# IMU和相机时间延时
# t_b = t_c + td
td_b_c: 0
# 激光相机外参 Pc = q_c_l * Pl + t_c_l
# q (x, y, z, w)
q_c_l: [0.5, 0.5, 0.5, 0.5]
t_c_l: [0, 0, 0]
# t_b = t_l + td
td_b_l: 0.0
lidar: # 激光参数 lidar_type: 1 # Livox: 1; Velodyne: 2; Ouster: 3 scan_line: 1 # 扫描线数 nearest_distance: 1 # 扫描盲区, m farthest_distance: 250.0 # 最远距离, m frame_rate: 25 # 帧率, Hz ####################################################
