Comparison of Di erent Kalman Filters for Application to Mobile Robotics

The problem of state estimation of the mobile robot's trajectory being a nonlinear one, the intent of this thesis is to go beyond the realm of the basic Extended Kalman Filter(EKF) and study the more recent nonlinear Kalman Filters for their application to the problem at hand. The various lters that I employ in this study are: • Extended Kalman Filter(EKF) • Iterated Extended Kalman Filter (IEKF) • Unscented Kalman Filter(UKF) and its various forms and alternate editions The Robot is given di erent trajectories to run on and the performance of the lters on each of these trajectories is observed. The intensity of process noise and measurement noise are also varied and the e ect they have on the estimates studied. The study also provides a comparison of the computational costs involved in each of the lters above. Pre-established numerically e cient and stable techniques to lower the computational costs of the UKF are employed and their performance in accuracy and computational time is observed and noted. The UKF is proven to be a better lter in terms of accuracy for the non-linear cases such as inertial navigation systems, but this thesis tests speci cally for the system dynamics of the Mobile Robot. The results that are obtained are quite contrasting to the otherwise belief that the UKF should give better accuracy.

Author

• Ravichandran, Suraj

• Kalman Filtering
• Nonlinear Kalman Filters
• Unscented Kalman Filters
• Mobile robotics
• Iterated Extend Kalman Filter
• Extended Kalman Filter

Identifier

hdl:1920/8997

OAI identifier oai:identifier

oai:MARS:1920/8997