Simultaneous information and global motion analysis ("SIGMA") for car-like robots

This paper proposes a new algorithm named "SIGMA" to address the problem of simultaneous information and global motion analysis for a car working in unstructured outdoor environments. The map of the environment is made by a Simultaneous Localization and Mapping (SLAM) algorithm that uses an Hybrid Metric Map (HYMM) structure for mapping. The path planning approach presents a global solution maximizing overall information gain of the map. The cost function used considers the present and future uncertainty in the map and vehicle and is based on the variation of the covariance matrix trace. Eigenvalue concepts are utilized to determine overall information change from the information matrix properties. An information graph is constructed followed by a search to find the optimal information-based rough path. Results are presented to demonstrate performance of the algorithm.