Comments (4)
Hi,
Thanks you for you interrest.
I did not hard coded trained parameters somewhere, they are included in iekfnets.p.
delta_p.p and normalize_factors.p are two files that avoid useless computation. If you delete it, the code will just compute them from data.
If you delete iekfnets.p, you will have a filter without training parameters. The filter still works well but it is better once trained (See Section V-D in the paper). This is particularly remarkable.
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@mbrossar Thanks for your reply!
I'm also wondering how did you set the parameters in class KITTIParameters?
cov_omega = 2e-4 cov_acc = 1e-3 cov_b_omega = 1e-8 cov_b_acc = 1e-6 cov_Rot_c_i = 1e-8 cov_t_c_i = 1e-8 cov_Rot0 = 1e-6 cov_v0 = 1e-1 cov_b_omega0 = 1e-8 cov_b_acc0 = 1e-3 cov_Rot_c_i0 = 1e-5 cov_t_c_i0 = 1e-2 cov_lat = 1 cov_up = 10
And these values are different compared to those in class Parameters in utils_numpy_filter.py, and also different from the initial values stated in the paper.
`# Process noise covariance
cov_omega = 1e-3
"""gyro covariance"""
cov_acc = 1e-2
"""accelerometer covariance"""
cov_b_omega = 6e-9
"""gyro bias covariance"""
cov_b_acc = 2e-4
"""accelerometer bias covariance"""
cov_Rot_c_i = 1e-9
"""car to IMU orientation covariance"""
cov_t_c_i = 1e-9
"""car to IMU translation covariance"""
cov_lat = 0.2
"""Zero lateral velocity covariance"""
cov_up = 300
"""Zero lateral velocity covariance"""
cov_Rot0 = 1e-3
"""initial pitch and roll covariance"""
cov_b_omega0 = 6e-3
"""initial gyro bias covariance"""
cov_b_acc0 = 4e-3
"""initial accelerometer bias covariance"""
cov_v0 = 1e-1
"""initial velocity covariance"""
cov_Rot_c_i0 = 1e-6
"""initial car to IMU pitch and roll covariance"""
cov_t_c_i0 = 5e-3
"""initial car to IMU translation covariance"""
`
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Hello,
I found your paper very interesting and thanks for sharing the codes! I test the filters by running main_kitti.py, and got similar results as in paper. However, I didn't find in codes where you load the trained model for test. I deleted the provided training parameters (delta_p.p, iekfnets.p, normalize_factors.p), and it still generates the same results. Did you hard coded your trained parameters somewhere?
Hello, Mr/Ms. Zhang!
Sorry to interrupt, I have tried to download KITTI IMU raw data many times, but I have been unable to succeed. Would you mind sharing it like Baidu Cloud?
Thank you!
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Hello,
You can find my reformatted kitti data in google drive : https://drive.google.com/open?id=1DClhQEDayv2p4IJ1a9XydF-FpnWh5_EQ
You can also find if required temp.zip at https://drive.google.com/open?id=1WPuC71kSb-dq0gSjrrSwWt8WM7XepKmd
Martin
Martin
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Related Issues (20)
- Issue on the ai-imu-dr with the compiling and running python main file. HOT 7
- Cannot access url for data and train parameters HOT 1
- train parameters size mismatch HOT 37
- Case 2011_10_03_drive_0027_extract - what happened?
- fixing the torch.gesv in utils_torch_filter.py HOT 3
- Train filter failure with error: TypeError: zeros() received an invalid combination of arguments - got (NoneType, int, int), HOT 6
- about sup.pdf HOT 5
- Code's equation not same with the paper HOT 21
- The checkpoint is not same with current model? Fail to load state_dict
- Reason for no end frame for some of the sequences
- Eq. 6 of Brossard's paper: Rotation matrix must be confined in x-y plane. HOT 6
- Issues with the comparison between the estimated position and ground-truth one
- Anyone knows what's the meaning of odometry_benchmark and odometry_benchmark_img? HOT 1
- Eq. 11 of the propagation step different from code (?) HOT 4
- unmatched iekfnets.p from dropbox HOT 5
- Asking for KITTI origin imu data(containing the origin timestamp file) of 09_30_0028
- IEKF nets NOT loaded
- Read data
- testing based on random weights also giving the same results as in paper HOT 9
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