Spatiotemporal Representation of Dynamic Scences We present a novel representation of dynamic scenes perceived by moving agents. This type of environments require constant updates of the map information that uses conventional geometric representations due to the changing relative position of dynamic object to the static scene. We show that this changed representation of the environment increases the robustness and accuracy in the perception of the scene and simplifies significantly the processing and complexity of the perception module. At the same time, the changed representation allows also a better prioritization of attention to the moving object around the robot that takes into account not the Euclidean distance but the timetointeraction (TTI), which is the core contribution of this approach. We present the mathematical framework behind the proposed representation and show examples, how this framework simplifies and robustifies the processing in the perception modules of a moving agent.

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.