Basic extended Kalman filter: simultaneous localisation and mapping

Abstract

The goal of the chapter is to provide an opportunity for researchers who are new to, or interested in, this exciting area of research with the basics, background information, major issues, and the state-of-the-art as well as future challenges in SLAM with a bent towards EKF-SLAM. It will also be helpful in realizing what methods are being employed and what sensors are being used. It presents the 2 – Dimensional (2D) feature based EKF-SLAM technique used for generating robot pose estimates together with positions of features in the robot’s operating environment, highlights some of the basics for successful EKF – SLAM implementation: (1) Process and observation models, these are the underlying models required, (2) EKF-SLAM Steps, the three-stage recursive EKF-SLAM process comprising prediction, observation and update steps, (3) Feature Extraction and Environment modelling, a process of extracting well defined entities or landmarks (features) which are recognisable and can be repeatedly detected to aid navigation, (4) Data Association, this consists of determining the origin of each measurement, in terms of map features, (5) Multi – Robot – EKF – SLAM, the two types of multi robot systems are described: Collaborative and Cooperative multi robot systems with more emphasis on the Cooperative SLAM Scheme.