Robot Localization and Kalman Filters ..
In this thesis I look at two large research elds.
I know I am very late to this post, and I am aware that this comment could very well go unseen by any other human eyes, but I also figure that there is no hurt in asking. This article was very helpful to me in my research of kalman filters and understanding how they work. I would absolutely love if you were to do a similar article about the Extended Kalman filter and the Unscented Kalman Filter (or Sigma Point filter, as it is sometimes called). If you never see this, or never write a follow up, I still leave my thank you here, for this is quite a fantastic article.
Airborne attitude estimation using a Kalman filter
Comparison between the unscented Kalman filter and the extended Kalman filter for the position estimation module of an integrated navigation information system Mathieu St-Pierre Electrical engineering
In this work we provide a thorough discussion of the robot localization problem and Kalman Filter techniques. First, we look at current methods to obtain location information, pointing out advantages and disadvantages. We formalize how to combine this information in a probabilistic framework and discuss several currently used methods that implement it. Second, we look at the basic concepts involved in Kalman Filters and derive the equations of the basic filter and commonly used extensions. We create understanding of the workings, while discussing the differences between the extensions. Third, we discuss and experimentally show how Kalman Filters can be applied to the localization problem. We look at system and measurement models that are needed by the filter; that is, we model a driving system, a GPS-like sensor, and a landmark-based sensor. We perform simulations using these models in our own general Kalman Filter simulator showing different behaviors when applying the Kalman Filter to the localization problem. In order to use the landmark-based sensor when it can not uniquely identify landmarks, we extend the Kalman Filter to allow for multiple beliefs.While localization is most commonly associated with GPS, many use cases remain where satellite-based navigation is too inaccurate or fails completely. In this seminar, we will present techniques usable for indoor localization of pedestrians. We will introduce several approaches using Inertial Measurement Units attached to the subject. Due to the strong drifting behavior of those units, several steps are necessary to provide feasible accuracy: the use of filter techniques and the use of Zero Velocity Updates. We will explain the required state-space
model and its application in recursive Bayesian filters like the Extended Kalman Filter or the Particle Filter. The use of aiding techniques is discussed and a map-aided, WiFi-initialized Particle Filter is presented.