论文标题
基于图的多传感器融合，以始终定位自主施工机器人
Graph-based Multi-sensor Fusion for Consistent Localization of Autonomous Construction Robots
论文作者
论文摘要
实现大规模施工机的自主操作，例如挖掘机，可以为在危险和危险环境中应用的人体安全和运营机会带来关键的好处。为了促进机器人自主权，稳健而准确的状态估计仍然是使这些机器在各种复杂环境集中运行的核心组成部分。在这项工作中，提出了一种用于机器人状态估计和本地化的多模式传感器融合的方法，从而在现实世界中实现了施工机器人的操作。提出的方法提出了一个基于图的预测循环，该循环结合了过滤和平滑的好处，以便以高更新速率提供一致的状态估计，同时维持大型地球移动挖掘机的准确全球定位。此外，所提出的方法可以灵活地集成异步传感器测量值，即使在传感器辍学阶段，也可以提供一致的姿势估计值。为此，提出了用于在两个不同的优化问题之间切换的双循环设计，直接解决了临时故障和随后的全球位置估计回报。提出的方法是在两名Menzi Muck行走挖掘机上实施的，并在代表性的操作环境中进行的现实测试中进行了验证。
Enabling autonomous operation of large-scale construction machines, such as excavators, can bring key benefits for human safety and operational opportunities for applications in dangerous and hazardous environments. To facilitate robot autonomy, robust and accurate state-estimation remains a core component to enable these machines for operation in a diverse set of complex environments. In this work, a method for multi-modal sensor fusion for robot state-estimation and localization is presented, enabling operation of construction robots in real-world scenarios. The proposed approach presents a graph-based prediction-update loop that combines the benefits of filtering and smoothing in order to provide consistent state estimates at high update rate, while maintaining accurate global localization for large-scale earth-moving excavators. Furthermore, the proposed approach enables a flexible integration of asynchronous sensor measurements and provides consistent pose estimates even during phases of sensor dropout. For this purpose, a dual-graph design for switching between two distinct optimization problems is proposed, directly addressing temporary failure and the subsequent return of global position estimates. The proposed approach is implemented on-board two Menzi Muck walking excavators and validated during real-world tests conducted in representative operational environments.
