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 | 1 | initial version |
It doesn't matter how accurate your sensor is if you adjust you covariance matrix accordingly. If your covariance matrix has values indicating that the noise in the sensor is smaller than it actually is than you will get odd behavior. I once had two sensors which had more noise than the covariance matrices allowed. This resulted in the robot jumping between the two poses from the two sensors.
That being said, while robot localization doesn't care about how accurate your sensors are, robot localization can only be as accurate as the information you are giving it. If you need accuracy within +/- 5cm than you are going to need a sensor or combination of sensors that are at-least that accurate. If you are fusing noisy GPS data with noisy IMU data you are going to get strange readings.
| | 2 | No.2 Revision |
It doesn't matter how accurate your sensor is if you adjust you covariance matrix for each sensor accordingly. If your covariance matrix has values indicating that the noise in the sensor is smaller than it actually is than you will get odd behavior. I once had two sensors which had more noise than the covariance matrices allowed. This resulted in the robot jumping between the two poses from the two sensors.
That being said, while robot localization doesn't care about how accurate your sensors are, robot localization can only be as accurate as the information you are giving it. If you need accuracy within +/- 5cm than you are going to need a sensor or combination of sensors that are at-least that accurate. If you are fusing noisy GPS data with noisy IMU data you are going to get strange readings.
EDIT
In response to the comments. Generally the process_noise_covariance and the initial_estimate_covariance default values found on the git repo should work fine.
If the sensor covariances are unknown do not set them to zero. This will make them trust the sensors too much. Instead, take a guess, try it out and modify them accordingly.If you want the robot localization to trust one sensor more decrease the covariance or vise versa.
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