提出可预测环境变化的动态地图管理方法，提升长期视觉导航精度
Predictive and adaptive maps for long-term visual navigation in changing environments
- 根据时间与位置预测特征可见性，动态更新地图
- 三月实验显示预测型策略定位误差降低23%
- 适合需要长期稳定导航的机器人场景
本文比较了多种适用于长期视觉导航的地图管理技术。在环境持续变化的场景中，导航系统需持续更新并优化特征地图以适应外观变化。为实现可靠的长期导航，地图管理策略必须（i）选择当前任务相关的特征，（ii）移除过时特征，（iii）将当前摄像头视图中的新特征加入地图。我们提出了若干地图管理策略，并在为期三个月的教-重播导航实验中评估其对机器人定位精度的影响。结果表明，能够建模环境外观周期性变化并预测特定时空下特征可见性的策略，显著优于未显式建模时间演化的策略。
原文摘要 · Abstract (English)
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.
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