Predictive and adaptive maps for long-term visual navigation in changing environments
This addresses the challenge of reliable navigation for robots in dynamic settings, though it is incremental as it builds on existing map management approaches.
The paper tackles the problem of long-term visual navigation in changing environments by comparing map management techniques, finding that strategies modeling cyclic appearance changes and predicting feature visibility improve robot localization accuracy over three months of experiments.
In this paper, we compare different map management techniques for long-term 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 long-term 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 long-term teach-and-repeat 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.