Shimraz/Kalman-Filters

Filtering Robots perceive the world through sensors, but sensors are inherently noisy making it challenging to observe environmental states completely. There is advance understanding of physical systems like kinematics and dynamics of different robots. This knowledge can provide state estimates, but mathematical models too are not perfect. The problem is as follows, given noisy sensor measurements and knowledge of the system, how to combine this information such that the results is better than the individual results. A well-known solution to this problem is the use of filter algorithms to suppress sensor noise and or fuse multiple sensors for a better state estimate. There are many filters, but the famous ones in robotics are kalman filter, complementary filter, low-pass filter, band-pass filter and high-pass filter. Although named differently, all these filters share a common foundation. Here, we will derive a generic filter.

MATLAB