Predictive and adaptive maps for long-term visual navigation in changing environments
🤖 AI Summary
This work addresses the challenge of degraded localization reliability in long-term visual navigation caused by persistent environmental appearance changes that render feature maps obsolete. To mitigate this issue, the authors propose a time-aware dynamic feature map management mechanism that models the periodic variations in scene appearance to predict which features are likely to be visible at specific spatiotemporal conditions. Leveraging these predictions, the system adaptively selects, prunes, and updates map features in real time. Evaluated over a three-month teach-and-repeat navigation experiment, the proposed approach significantly outperforms conventional static mapping strategies, demonstrating substantial improvements in robotic localization accuracy within temporally varying environments.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
long-term visual navigation
changing environments
map management
feature map adaptation
environment appearance change
Innovation
Methods, ideas, or system contributions that make the work stand out.
predictive mapping
adaptive map management
long-term visual navigation
environment appearance change
feature selection
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