This chapter is the conclusion of the book. It is devoted to providing an overview of emerging paradigms that are appearing as outstanding the traditional approaches in scalability or efficiency, such as hierarchical sub-mapping, or hybrid metric-topological map models. Other techniques not based on Bayesian filtering, such as iterative sparse least-squares optimization (Graph-SLAM and Bundle adjustment), are also introduced due to their efficiency and increasing popularity.
TopChapter Guideline
Top1. Introduction
This book has explored in detail the most widely used approaches to Bayesian localization and SLAM in small to moderately sized scenarios, with the intention of revealing their mathematical foundations. But as the reader should have realized at this point, there exists no such thing as a perfect or “magic” approach to localization or SLAM that works in all cases for any kind of sensor and operation conditions. That limitation is more patent in SLAM than in localization, due to the more complex nature of the former estimation problem. While exposing each of the SLAM algorithms described in chapter 9, we stated the advantages of each method in contrast to the rest, but also insisted in their unique drawbacks. In this chapter we will reason further about those problems and will introduce different alternatives, out of the recursive Bayesian framework, that have been proposed in the literature to mitigate them in cases where the environment of the robot or the state-space of the problems are large or particularly complex. The objective is to offer the reader a wide perspective of the most relevant ideas present in the newest research, and also to serve as a complement to the rest of the book
In order to better realize the problems with all the methods for metric SLAM exposed so far when we augment the dimension or complexity of the mathematical setting, we could imagine what would be an “ideal,” “perfect” solution for enabling SLAM in mid or large-sized complex environments over extended periods of time. We certainly believe that this goal should comprise: