Constrained initialisation for bearing-only SLAM

Abstract

Simultaneous Localisation And Mapping (SLAM) is a stochastic map building method which permits consistent robot navigation without requiring an a priori map. The map is built incrementally as the robot observes the environment with its on-board sensors and, at the same time, is used to localise the robot. Typically, SLAM has been performed using range-bearing sensors, but the development of a SLAM implementation using only bearing measurements is desirable as it permits the use of sensors such as CCD cameras, which are small, reliable and cheap. However, bearing-only SLAM is hindered by the feature initialisation problem, where the estimated location of a new map landmark cannot be determined from a single measurement, and combined information from multiple measurements may be ill-conditioned. This paper presents a solution to the feature initialisation problem called constrained initialisation, which defers the use of sensor information until initialisation becomes well-conditioned. Measurements may be used out-of-sequence and all the available information can be incorporated without inconsistency. Furthermore, this method operates within the conventional extended Kalman filter (EKF) framework of the SLAM algorithm.