Monte Carlo localization for robots using dynamically expanding occupancy grids

The past few years have seen tremendous growth in the research areas of Mobile Robotics. While growth has been fast and several problems have been very splendidly solved most mobile roboticists are faced with two primary challenges: how will the robot gather information about its environment and how will it know where it is? These two problems are referred to as:
(i). Mapping and
(ii). Localization.
Mapping is the process whereby a robot can extract relevant information from its environment allowing it to ”remember” it. Localization is using this stored map to move about in the environment with a clear sense of direction because the robot knows where it is, by referring to the map. Localization is the problem of estimating a robot’s pose relative to a map of its environment. However, both these problems are computationally intensive to solve and furthermore, limitations on a robot’s on board computational abilities and inaccuracies in sensor hardware and motor effectors make it even harder. Most mapping techniques are limited by memory and hence a robot has a limitation on the area that it can directly map. Also, if the mapped area is extended, most mapping implementations require that the mapping parameters be changed and the entire mapping algorithm be executed again. However, in recent times a new mapping technique was explored which is that of using Dynamically Expanding Occupancy Grids (Ellore 2002), and of using a Centralized Storage System (Barnes, Quasny, Garcia, and Pyeatt 2004). By using this approach, the robot has virtually unlimited storage space and a small initial map which grows as the robots explores its environment.

Localization has not yet been attempted using Dynamically Expanding Occupancy Grids and a Centralised Storage System. This research is geared towards implementing Monte-Carlo Localization methods (Fox, Burgard, Dellaert, and Thrun 1999; Dellaert, Fox, Burgard, and Thrun ; Thrun, Fox, Burgard, and Dellaert 2001; Fox, Thrun, Burgard, and Dellaert 2001) to robots using Dynamically Expanding Occupancy Grids. By using this approach this research aims to provide a complete mapping and localization implementation for robots using dynamically expanding occupancy grids and a centralized storage system.