Robust Outdoor Selflocalization In Changing Environments In outdoor scenarios changing conditions (e.g., seasonal, weather and lighting effects) have a substantial impact on the appearance of a scene, which often prevents successful visual localization. The application of an unsupervised Slow Feature Analysis (SFA) on the images captured by an autonomous robot enables selflocalization from a single image. However, changes occurring during the training phase or over a more extended period can affect the learned representations. To address the problem, we propose to join longterm recordings from an outdoor environment based on their position correspondences. The established hierarchical model trained on raw images performs well, but as an extension, we extract Fourier components of the views and use that for learning of spatial representations, which reduces the computation time and makes it adequate to run on an ARM embedded system. We present the experimental results from a simulated environment and realworld outdoor recordings collected over a full year, which has effects like different day time, weather, seasons and dynamic objects. Results show an increasing invariance w.r.t. changing conditions over time, thus an outdoor robot can improve its localization performance during operation.

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.