One of the most popular sensor fusion algorithms is the Kalman filter. It is used to merge the data from various autonomous vehicle sensors. The Kalman filter was invented in 1960 by Rudolph Kalman. It is used to track navigation signals, as well as phones and satellites.
The main application of the Kalman filter is data fusion, which is used to estimate the state of a dynamic system in the present, past, and future. It can be used to monitor a moving pedestrian's location and velocity over time, and also to quantify their associated uncertainty. In general, the Kalman filter consists of two iterative steps:
- Predict
- Update
The state of a system is calculated using a Kalman filter and is denoted as x. This vector is composed of a position (p)and a velocity (v), while the measure of uncertainty...