karanchawla / gps_imu_kalman_filter Goto Github PK
View Code? Open in Web Editor NEWFusing GPS, IMU and Encoder sensors for accurate state estimation.
License: Apache License 2.0
Fusing GPS, IMU and Encoder sensors for accurate state estimation.
License: Apache License 2.0
Hello,how can I compile this project and use it on my computer?
hello,
Is there any papers about your work? If yes, can you give me a URL or paper name?
Thkan you.
do you know public dataset that can prove my algorithm works
Dear Karanchawla, thank you for sharing useful code. Currently, I am looking to fuse the GPS and IMU data for localization self-driving cars. Your fusion approach looks useful for my needs. But am unable to understand, how to make use of this package without any test case. As i checked in the main loop, there is no function to read the data to start the fusion process.
Could you please, give little more detail about how to start the fusion process? really It will be more helpful to further improve this.
regards,
Ajay
Thank you for sharing.
Can you upload some example data or give a description of the input data format?
Urgently need your reply.
Thank you again.
when I read the code, I find that there are lack of a head file "csv_reader.hpp".Can you update it and make this project integrated.
There are also lack of a definition of "calculate_jacobian()" in the member function of "void EKF::updateJA(const double dt)" in ekf.cpp.
Sir, can you help me fix these problems. thank you very much.
where is the calculate_jacobian function ?
I would have expected the read_imu_data and the read_gps_data functions to be called in the loop . May be in the function loop in run_fusion.cpp but I dont see it being called anywhere. How is the data being read from the sensors to get measurements across time intervals.
or am I missing something?
Do you expect all the readings to be available before hand or can this also work in real time ?
Can you share one example data as descripted in main.cpp? Thanks a lot.
Thank you for sharing ! I have a question about your ekf algorithm. I don't know what state variables are you choose and how to get the state transfer equation and measurement equation ? Can you give me some advice and papers about your algorithm ?
I runned the filter with several data sets and find out that the GPS state is totaly out of state.
The acceleration and velocity are correct but not the GPS and i don't succed in finding out what's the problem...
Pls help me =(
I'll link a picture of what's happening (red is the datas and blue the state).
I'm glad to see your sharing. After I wrote the main function call myself, when I ran with the dataset, I found that the result was totally wrong (compared with Python's result). Is this finished product, or is it partially incomplete? I was confused.
Finally, the output state is found as follows.
another question ,I've noticed your approach to Kalman time DT, which I'm confused about.
Anyway, thank you a lot!
As a function is written as:
void GpsIns::loop()
{
//m.lock();
std::lock_guardstd::mutex lock(m);
set_data();
filter->process(*_sensor_data);
_prev_gps_counter = _gpscounter;
//m.unlock();
}
The function above is being introduced as "Main loop for the filter". I am confused about the functions:read_gps_data, read_imu_data, read_encoders seem not working here ,and some counters are forgot to plus "1". Is there some work should be done by ourselves and you simplify the main loop here? Thanks, just too confused.
In calculate_jacobian function, r13 should be turn_radius * ( cos(dt*psi_dot + psi) - cos(psi) ).
Yes, I will upload it once I get home tonight.
Originally posted by @karanchawla in #2 (comment)
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