Bearing-Only Vision SLAM with Distinguishable Image Features

One of the key competences for autonomous mobile robots is the ability to build a map of the environment using natural landmarks and to use it for localization (Thrun et al., 1998, Castellanos et al, 1999, Dissanayake et al, 2001, Tardos et al. 2002, Thrun et al., 2004). Most successful systems presented so far in the literature have relied on range sensors such as laser scanners and sonar sensors. For large scale, complex environments with natural landmarks the problem of SLAM is still an open research problem. Recently, the use of vision as the only exteroceptive sensor has become one of the most active areas of research in SLAM (Davison, 2003, Folkesson et al., 2005, Goncavles et al., 2005, Sim et al., 2005, Newman & Ho., 2005). In this chapter, we present a SLAM system that builds maps with point landmarks using a single camera. We deal with a set of open research issues such as how to identify and extract stable and well-localized landmarks and how to match them robustly to perform accurate reconstruction and loop closing. All of these issues are central to success, especially when an estimator such as the Extended Kalman Filter (EKF) is used. Robust matching is required for most recursive formulations of SLAM where decisions are final. Even for methods that allow the data associations to change over time, e.g. (Folkesson & Christensen, 2004, Frese & Schroder 2006), reliable matching is very important. One of the big disadvantages with the laser scanner is that it is a very expensive sensor. Cameras, on the other hand, are relatively cheap. Another aspect of using cameras for SLAM is the much greater richness of the sensor information as compared to that from, for example, a range sensor. Using a camera it is possible to recognize features based on their appearance. This provides the means for dealing with one of the most difficult problems in SLAM, namely data association. The main contributions of this work are i) a method for the initialisation of visual landmarks for SLAM, ii) a robust and precise feature detector, iii) the management of the measurement to make on-line estimation possible, and iv) the demonstration of how this framework can facilitate real-time SLAM even with an EKF based implementation.