This week, we worked on Kalman Filter.
What is Kalman Filter?
“In statistics and control theory, Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by estimating a joint probability distribution over the variables for each timeframe. The filter is named after Rudolf E. Kálmán, one of the primary developers of its theory.”
The Kalman filter has numerous applications in technology. A common application is for guidance, navigation, and control of vehicles, particularly aircraft and spacecraft. Furthermore, the Kalman filter is a widely applied concept in time series analysis used in fields such as signal processing and econometrics. Kalman filters also are one of the main topics in the field of robotic motion planning and control, and they are sometimes included in trajectory optimization.”
So how we use this Kalman Filter? We have 4 cameras set over our field and each one takes a view of the field in 60FPS and sends it to software called SSL-Vision. Our robots communicates and takes the data that came from SSL-Vision and filters it.
When the ball goes across the point of interception of 4 cameras, which is the center of the field, all cameras are seeing the ball and giving different coordinates about the ball’s place in the field. And that sometimes causes our robots to freeze or move through to ball from wrong direction. This week we worked to minimize these problems.