Transformation Between Simple and Detailed Maps Based on Line Matching for Robot Navigation Most autonomous mobile systems require pre generated detailed maps that are expensive to prepare. In this study, we proposed an autonomous mobile system that does not rely on a detailed map; instead, it uses a simple map for robot navigation. The robot has two maps; one is a detailed map created by sensor observation during movement, and the other is a simple map provided as preinformation. The robot performs the matching between detailed and simple maps and converts waypoints on the simple map to waypoints on the detailed map. Matching is performed based on straight line matching and is optimized by genetic algorithm. Experiments were conducted in buildings. Our method is compared with linear transformations and conditions that our method works effectively or not are confirmed.

Predictive and adaptive maps for longterm visual navigation in changing environments In this paper, we compare different map management techniques for longterm visual navigation in changing environments. In this scenario, the navigation system needs to continuously update and refine its feature map in order to adapt to the environment appearance change. To achieve reliable longterm navigation, the map management techniques have to (i) select features useful for the current navigation task, (ii) remove features that are obsolete, (iii) and add new features from the current camera view to the map. We propose several map management strategies and evaluate their performance with regard to the robot localisation accuracy in longterm teachandrepeat navigation. Our experiments, performed over three months, indicate that strategies which model cyclic changes of the environment appearance and predict which features are going to be visible at a particular time and location, outperform strategies which do not explicitly model the temporal evolution of the changes.