Predictive and adaptive maps for long-term visual navigation in changing environments

TL;DR

This paper evaluates various map management strategies for long-term visual navigation in changing environments, emphasizing the importance of modeling environmental cyclic changes to improve localization accuracy over time.

Contribution

It introduces and compares new map management strategies that explicitly model environmental cyclic changes for enhanced long-term navigation performance.

Findings

Strategies modeling cyclic environmental changes outperform others.

Predictive feature visibility improves localization accuracy.

Long-term experiments over three months validate the approach.

Abstract

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.