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The code of another article #34
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@Iqun1314 Hi, I am re-implementing RINS-W. Do you download the urban dataset and generate the ground-truth of RINS-W? I don't know how to generate the ground-truth of RINS-W by author. But according to my understanding of this article, the statistics I get are very different from the reported by the author. So, I hope to be able to communicate if you are re-implementing RINS-W. |
Ok,I will. |
This is the paper of this repo. The code to extract the dataset is based on pyKitti. Regarding the coe of RINS-W, we expect to put it online soonely :) |
Thank you for your reply. I'm looking forword to the code of RINS-W. |
@mbrossar Hi Martin, I am also very curious to know the details of the implementation of the RINS-W paper. The odometry performance is really good of RINS-W. Following up on the same ask since I saw your reply in March, do you plan to release the code soon in the near future? If so, roughly about when should we expect? |
I have put the code here, thank you for recalling me. There are few improvement with the paper, e.g. using CNN rather than LSTM, I let you see. |
@mbrossar Oh, thanks for such a prompt response! That was much quicker than I was expecting. Also thanks for uploading the code! I see you create the other repo yesterday/today. Seems that the code in that repo is not complete. In one file I see
While lie_algebra.py file is there, I did not find iekf.py and utils.py files in the repo. Just to confirm with you. |
No, it is the opposite, I mean that CNN network has better performance than LSTM. |
@mbrossar now I can see all the files, thanks! Maybe "performance" was not the right word I should use. In the README file, you said the CNN is faster to train than LSTM. I am curious if LSTM has better prediction accuracy than CNN. Could you comment on that from the validation test you did? |
I obtained better prediction accuracy with the CNN along with better time execution. In the readme, I indicate CNN is faster to train than LSTM, which is in fact true. As performance of LSTM and CNN depends on hyperparameter, I can just say that, on my experiments, CNN were better. |
Can you confirm if Brossard's code on: https://github.com/mbrossar/RINS-W has the python tools by which to convert a dataset in KITTI format into Brossard's |
@Iqun1314 From your posts, I think you had access to the training data and the trained model. Apparently, many followers of AI-IMU DR including the author Martin are looking for these data and model. |
I found out that Brossard's code has the means to transform KITTI files into Brossard's pickle files which are composed of 5 python dictionaries apart from time. It is within main_kitty.py. Now his pickle file data dictionaries are made of u (accel+gyro), and quantities like p_gt which depend on lat/lon , v_gt is velocity Rot_gt are rotation matrices depending on roll, pitch and yaw. These are indexed and synchronized with the timestamp dictionary 't'. What I don't know is how GPS outage is modeled. Brossard's original data could tell us that. How do you work a test case where you only have u? ie. u and no GPS? For KITTI files, the standard is to replace the last 3 integer numbers with -1, but Brossard's code ignores such numbers. Can anyone tell me out GPS outage is modeled insofar as the test pickle files are concerned? |
Pickle files are now available at new url, please see the readme |
Hi,
First thanks for your great work and open source.
I have downloaded your code and run it on my laptop, the result is really cool. But I notice your another article "RINS-W: Robust Inertial Navigation System on Wheels" , I have not found the code.
So, if you can upload the code of the article "RINS-W: Robust Inertial Navigation System on Wheels", I will be deeply grateful .
Thank you very much.
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