Using multiple SLAM algorithms

Simultaneous localisation and map building (SLAM) is one of the most important and challenging areas of mobile robotics. Unfortunately, the optimal Kalman filter solution incurs computational costs that scale quadratically with the number of beacons, which is prohibitive for many real time and large scale applications. Consequently, there is a significant practical need for more efficient approaches. The challenge is to develop methods that are both efficient and mathematically rigorous. In this paper we show that the full SLAM problem can be decomposed into two distinct mathematical operations. One is the maintenance of global state information for both the vehicle and the beacons, and the other is the maintenance of relative state information. These operations are distinct because the former is an unobserservable estimation problem while the latter is not. We argue that solutions to these two problems can be applied as scaffolding for the development of a wide variety of specialized SLAM algorithms. As a practical demonstration of the power of the two operations when applied as a generic solution to the SLAM problem, we provide empirical results for a scenario requiring the real-time construction and maintenance of a map containing 1.1 million beacons.