Simultaneous localization and mapping with robots

Abstract: A fundamental competence of any mobile robot system is the ability to remain localized while operating in an environment. For unknown/partially known environments there is a need to combine localization with automatic mapping to facilitate the localization process. The process of Simultaneous Localization and Mapping (SLAM) is the topic of this thesis.SLAM is a topic that has been studied for more than 2 decades using a variety of different methodologies, yet it deployment has been hampered by problems in terms of computational complexity, consistent integration of partially observable features, divergence due to linearization of the process, introduction of topological constraints into the estimation process, and efficient handling of ambiguities in the data-association process. The present study is an attempt to address and overcome these limitations.Initially a new model for features, inspired by the SP-map model, is derived for consistent handling of a variety of sensor features such as point, lines and planes. The new feature model enable incremental initialization of the estimation process and efficient integration of sensory data for partially observable features. The new feature model at the same time allow for consistent handling of all features within a unied framework.To address the problems associated with data-association, computational complexity and topological constraints a graphical estimation method is de- rived. The estimation of features and pose is based on energy optimization. Through graph based optimization it is possible to design a feature model where the key non-linearities are identified and handled in a consistent man- ner so as to avoid earlier discovered divergence problems. At the same time any-time data-association can be handled in an efficient manner. Loop closing in the new representation is easily facilitated and the resulting maps show superior consistency even for large scale mapping problems.The developed methods have been empirically evaluated for SLAM using laser and video data. Experimental results are provided both for in-door and out-door environments.The methods presented in this study provide new solutions to the lin- earization problem, feature observability, any-time data association, and integration of topological constraints.