预测性与自适应地图用于变化环境中的长期视觉导航
Predictive and adaptive maps for long-term visual navigation in changing environments
- Artificial Intelligence Center, Czech Technical University（捷克技术大学人工智能中心）
- Technical University of Cartagena, Spain（西班牙卡塔加纳技术大学）
- Australian Centre for Robotic Vision, QUT（澳大利亚机器人视觉中心，昆士兰大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文比较了不同地图管理技术用于变化环境中的长期视觉导航，提出策略通过建模环境外观的周期性变化以提升机器人定位精度。
AI中文摘要：
在本文中，我们比较了不同地图管理技术用于长期视觉导航在变化环境中的应用。在该场景中，导航系统需要持续更新和优化其特征地图以适应环境外观的变化。为了实现可靠的长期导航，地图管理技术必须（i）选择对当前导航任务有用的特征，（ii）移除过时的特征，（iii）并将当前摄像头视图中的新特征添加到地图中。我们提出了几种地图管理策略，并评估了它们在长期教与重复导航中机器人局部定位精度方面的性能。我们的实验在三个月内进行，表明能够建模环境外观周期性变化并预测特定时间和位置可见特征的策略，优于不显式建模变化时间演化的策略。
英文摘要：
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.
