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 | 1 | initial version |
Strongly seconding @dornhege's comment.
The kalman filter is already doing the integration on the accelerations provided by the IMU, and using that to update its state estimate, so you're not going to get any additional accuracy by trying to spoof your own position estimates from the same data and feeding them to the ekf.
In order to improve your state estimation, I'd recommend doing a little reading on how kalman filters work (my favorite simple explanation was in Probabilistic Robotics, I can't recommend a freely-available one off the top of my head), and then making sure that you have the right covariances for all of its inputs. If you want to improve it beyond that, you'll need to invest in better/different sensors.
| | 2 | No.2 Revision |
Strongly seconding @dornhege's comment.
The kalman filter is already doing the integration on the accelerations provided by the IMU, and using that to update its state estimate, so you're not going to get any additional accuracy by trying to spoof your own position estimates from the same data and feeding them to the ekf.
In order to improve your state estimation, I'd recommend doing a little reading on how kalman filters work (my favorite simple introductory explanation was in Probabilistic Robotics, I can't recommend a freely-available one off the top of my head), and then making sure that you have the right covariances for all of its inputs. If you want to improve it beyond that, you'll need to invest in better/different sensors.
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