Difference between revisions of "Data Filtering Systems"
(added a blurb about the overall system and about Kalman filters) |
(added headings for Particle Filter and Models) |
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The purpose of the Data Filtering System is to take in noisy data from the various sensors (via the Data Acquisition System) and to form an accurate and cohesive picture of the robot's surroundings. It will then pass that picture to the Path Planning System. | The purpose of the Data Filtering System is to take in noisy data from the various sensors (via the Data Acquisition System) and to form an accurate and cohesive picture of the robot's surroundings. It will then pass that picture to the Path Planning System. | ||
| − | == Kalman Filter == | + | |
| + | == Filters == | ||
| + | === Kalman Filter === | ||
The [http://en.wikipedia.org/wiki/Kalman_filter Kalman filter] is a relatively simple method for determining parameters of a system from noisy measurements of that system. It will be a good start for future studies in more complicated methods. It first assumes that all of the noise in the system is Gaussian and then recursively estimates the unknown variables. To implement this we will need to make measurement models for each of the sensors and the model for the motors. This will involve a lot of discussion with the Environment Data Processing System to negotiate what kind of data will be sent from the camera and LIDAR. | The [http://en.wikipedia.org/wiki/Kalman_filter Kalman filter] is a relatively simple method for determining parameters of a system from noisy measurements of that system. It will be a good start for future studies in more complicated methods. It first assumes that all of the noise in the system is Gaussian and then recursively estimates the unknown variables. To implement this we will need to make measurement models for each of the sensors and the model for the motors. This will involve a lot of discussion with the Environment Data Processing System to negotiate what kind of data will be sent from the camera and LIDAR. | ||
| + | |||
| + | === Particle Filter === | ||
| + | |||
| + | == Models == | ||
Revision as of 11:23, 19 September 2007
The purpose of the Data Filtering System is to take in noisy data from the various sensors (via the Data Acquisition System) and to form an accurate and cohesive picture of the robot's surroundings. It will then pass that picture to the Path Planning System.
Filters
Kalman Filter
The Kalman filter is a relatively simple method for determining parameters of a system from noisy measurements of that system. It will be a good start for future studies in more complicated methods. It first assumes that all of the noise in the system is Gaussian and then recursively estimates the unknown variables. To implement this we will need to make measurement models for each of the sensors and the model for the motors. This will involve a lot of discussion with the Environment Data Processing System to negotiate what kind of data will be sent from the camera and LIDAR.
