Very nice work! Thank you for sharing. Any additional advise on tuning the prediction noise parameter? In my case ROVIO seems to be stable but the trajectory seems to have a scaling issue depending on how fast I move. Your help is appreciated!
Thanks
With monocular odometry, scale is dependant of IMU and with cheap MEMS units that we are using, device parametrisation is an artā¦
Depending on the use case, you can get good (ā¦better) results when using the APM EKF filter as vision_position_estimate will be fused with the other states.
Thank ppoirier for the quick answer. You set the bar for parameterization pretty high. Any recommendation of depthType setting considering a cheaper IMU in my case?
May I suggest that you ask directly on the ROVIO github ?
Itās been a long time and I dont have a working setup atm.
Regards
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Hello there. Can i use the intel T265 as the camera for testing various VIO algorithm? And do I still require to calibrate the T265 manually? Thank you
Yes you can , in that case you eextract video and IMU data using Realsense API.
You can look at @LuckyBird blog on April Tag for example
why not use the imu in FC directly? In fact, I meet some trouble when I use the imu in FC, the frequency of imu canāt be improved to 200hz.
A directly connected IMU is easier to sync as it is interrupt driven and just need a simple routine to decode with fixed delay. For FC based IMU you need to decode Mavlink message and associated time code to sync properly.
Other advantage is the tight and fixed integration of the VIO unit, making it much easier to calibrate.
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