View management for lifelong visual maps The time complexity of making observations and loop closures in a graphbased visual SLAM system is a function of the number of views stored 1, 2. Clever algorithms, such as approximate nearest neighbor search, can make this function sublinear. Despite this, over time the number of views can still grow to a point at which the speed andor accuracy of the system becomes unacceptable, especially in computation and memoryconstrained SLAM systems. However, not all views are created equal. Some views are rarely observed, because they have been created in an unusual lighting condition, or from low quality images, or in a location whose appearance has changed. These views can be removed to improve the overall performance of a SLAM system. In this paper, we propose a method for pruning views in a visual SLAM system to maintain its speed and accuracy for long term use.

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.