论文标题
最低成本多功能用于不正确的地标边缘检测姿势大满贯
Minimum Cost Multicuts for Incorrect Landmark Edge Detection in Pose-graph SLAM
论文作者
论文摘要
姿势 - 格拉普（Pose-Graph）大满贯是事实上的标准框架，用于从视觉机器人导航期间的相对观察和动作的多课程体验构建大规模地图。在最近的高级SLAM框架（例如图形神经大满贯）的背景下，它受到了越来越多的关注。剩下的挑战是地标性错误识别错误（即不正确的地标边缘），可能会对推断的姿势图表产生灾难性影响。在这项研究中，我们介绍了使用新的健壮图切割技术在姿势图中最大化全球一致性的全面标准。我们的关键想法是将问题提出为最低成本的多切割，使我们不仅可以优化地标的信件，而且还可以优化地标数量，同时允许不同的地标。这使得我们提出的方法在具有里程碑意义的测量，图形拓扑和度量信息（例如机器人运动速度）的类型上不变。所提出的图形切割技术被整合到实用的SLAM框架中，并使用公共NCLT数据集对实验进行了验证。
Pose-graph SLAM is the de facto standard framework for constructing large-scale maps from multi-session experiences of relative observations and motions during visual robot navigation. It has received increasing attention in the context of recent advanced SLAM frameworks such as graph neural SLAM. One remaining challenge is landmark misrecognition errors (i.e., incorrect landmark edges) that can have catastrophic effects on the inferred pose-graph map. In this study, we present comprehensive criteria to maximize global consistency in the pose graph using a new robust graph cut technique. Our key idea is to formulate the problem as a minimum-cost multi-cut that enables us to optimize not only landmark correspondences but also the number of landmarks while allowing for a varying number of landmarks. This makes our proposed approach invariant against the type of landmark measurement, graph topology, and metric information, such as the speed of the robot motion. The proposed graph cut technique was integrated into a practical SLAM framework and verified experimentally using the public NCLT dataset.
