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Running R-VIO on the Kitti dataset #23
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First of all, thanks for your interest, @JanOpper. Yes, the IMU parameters play a very important role in the visual-inertial estimation, because those values will contribute to the covariance which is the key component for computing the state update. It would cause some critical problem if the IMU parameters are set with random numbers, as shown in your result, because this may incorrectly change the uncertainty about IMU measurement in the sense of sensor fusion with Kalman filter. |
Thank you for your feedback. |
I have another question: To what extent does the algorithm depend on time-synchronized data? Because I noticed that EUROC is actually time synchronized, whereas Kitti is not. |
Currently our algorithm requires the results of both spatial and temporal calibrations i.e., the data time offset between camera and IMU is needed (as listed in the config file). So if this value is not set properly, the performance shall not be guaranteed. Especially, if the data rates of camera and IMU are too different, the influence of this value will become more significant which could lead to the result that you have shown. |
Both the IMU and the camera have absolute timestamps, so there should be no fixed time offset. However, the camera was coupled to the lidar and not to the IMU, i.e. the photos were taken when the lidar pointed forward. This can lead to some jitter-like fluctuations in the camera frequency. |
Very glad to see this result @JanOpper. The threshold for motion detection is tunable (marked in the config file) depending on the sensitivity of IMU. However, the Sampson error is used for the ransac which is also tunable, so maybe you can try to increase that value instead to allow more features to be involved. While recently I have not found time to test this dataset myself, if it is possible, could you share with me the config file and data you are using so that I can try it sometime. |
Interestingly I had to decrease the inlier threshold for the ransac, because there were too many features included. Regardless of this, the code ran just as well as without the Sampson error. And thank you again for your support. |
@JanOpper |
Hi,
first of all thank you for sharing your code with the community!
I am trying to apply the code to the kitti raw dataset, specifically a residential recording (2011_10_03_drive_0027).
I am starting the estimation from a point where the car is at standstill und doing a right turn afterwards, because I was hoping the gravity and bias estimation would benefit from this.
I already found out that the accuracy is highly dependent on the IMU parameters, but unfortunately I couldn't find the actual values for kitti. I tried to estimate them myself via an Allan standard deviation plot from a few seconds of standstill data from a different track, but from my understanding this is only sufficient for the white noise values, but not the bias random walk. I therefore guessed an appropriate value for the latter.
I also found out that I need much higher white noise values than calculated for the turns to be recognized at all, about two orders of magnitude (used sigma_g: 0.0046, sigma_a: 0.06).
As you can see in the attached screenshot the algorithm is doing massive position corrections at each 90 degree turn. I am assuming this is because the uncertainty is reduced when the car is turning. But I am wondering why the uncertainty und deviation is so high in the first place. From what I have seen in your paper, you had much less deviation and corrections when you run the code on your urban driving test data.
I was hoping maybe you have an idea why the performance is not as good as in your paper and how I might improve this.
I will happily provide further details of the configuration or data I have used, if necessary.
Thank you very much!
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