Gaining by forgetting

Abstract

The emerging of mobile robots in everyday life scenarios, such as in the case of domestic care robots, is highly anticipated. Much research has been carried out to make robots more capable of performing tasks in our everyday environments. Despite major progress over the last decades, many hurdles are still to be taken. In the field of robotic mapping, which studies how robots can generate a map (internal representation) of their environment, modern Simultaneous Localization and Mapping (SLAM) methods allow robots to map their environment and be aware of where they are in that environment. Such maps can then be used for robot navigation, which allows a robot to travel from one place to another safely and autonomously. State of the art SLAM methods still show large limitations in their real world applicability. First and foremost, they are limited in the size of environment they can handle as maps grow inconsistent when environments get too large, or they cannot handle multi-story buildings for example because they are designed to only map in 2D. Performance even becomes significantly worse if one limits oneself to using affordable sensors. Secondly, modern SLAM algorithms still struggle with the tasks of building a map that is metrically consistent with the real world (that is, the map and a ground truth floor plan should align). Thirdly, the generated maps show obstacles (like walls), but do not give any other semantic details on them. For example, the map does not tell what places are rooms and what places are a corridor. In this thesis, it is investigated how robotic mapping and robot navigation could benefit from a human inspired approach to these tasks. Humans do not create floor plans, but remember their environments in terms of concepts. These concepts are then linked in a relative way, and places are connected by fuzzy, relative defined connections. The relatively new study of semantic mapping aims at integrating these concepts (semantics) into robotic mapping. However, so far these systems have been built on top of a traditional SLAM method. Parallel to this new development of semantic mapping, this thesis proposes an architecture, which we named LEMTOMap (Large Environment Metric TOpological Mapping system), that generates and handles maps in a relative way. It specifies mapping, localization and navigation in a way in which metric consistency of the map is no longer a requirement on a larger scale (e.g. that of a faculty building or larger). The main contributions of this thesis are captured by the LEMTOMap architecture. LEMTOMap introduces a new topological mapping paradigm that allows the robot to generate a map that is metrically consistent on a local scale, but does not require metric consistency on a larger scale. This way, the main challenge of modern SLAM - limiting metric inconsistency - is reduced to a challenge of subordinate importance. Additionally, a new grid map SLAM algorithm is introduced, named Rolling Window GMapping (RW-GMapping). To verify the expected performance enhancements of the LEMTOMap system architecture, LEMTOMap has been partially implemented and tested in simulated experiments. The experiments confirm the main benefits of LEMTOMap, mostly in terms of improved overall time and space complexity. The thesis concludes with a range of advices for future work. Part is aimed at the further implementation of LEMTOMap, and part at improving LEMTOMap beyond its current specification. Also, a performance issue of the original GMapping algorithm was detected and suggestions are made on how this should be improved.