论文标题
使用期望最大化在多机器人视觉猛击中的统计异常值识别
Statistical Outlier Identification in Multi-robot Visual SLAM using Expectation Maximization
论文作者
论文摘要
本文介绍了一种新颖的分布式方法，用于检测同时定位和映射（SLAM）中的映射间环闭合离群值。所提出的算法不依赖良好的初始化，并且一次可以处理两个以上的地图。在多机器人大满贯应用中，由不同试剂制作的地图具有非相同的空间参考框架，这使得在存在异常值的情况下初始化非常困难。本文提出了一种概率方法，用于通过检查旋转测量的几何一致性在姿势图优化之前检测不正确的方向测量值。预期最大化用于微调模型参数。作为辅助贡献，提出了一种新的近似离散推理程序，该过程使用图中的循环中的证据，并基于优化（乘数的替代方向方法）。与信仰的传播相比，该方法产生的结果优越，并且具有融合保证。给出了仿真和实验结果，以评估离群检测方法的性能以及关于合成和现实世界数据的推理算法。
This paper introduces a novel and distributed method for detecting inter-map loop closure outliers in simultaneous localization and mapping (SLAM). The proposed algorithm does not rely on a good initialization and can handle more than two maps at a time. In multi-robot SLAM applications, maps made by different agents have nonidentical spatial frames of reference which makes initialization very difficult in the presence of outliers. This paper presents a probabilistic approach for detecting incorrect orientation measurements prior to pose graph optimization by checking the geometric consistency of rotation measurements. Expectation-Maximization is used to fine-tune the model parameters. As ancillary contributions, a new approximate discrete inference procedure is presented which uses evidence on loops in a graph and is based on optimization (Alternate Direction Method of Multipliers). This method yields superior results compared to Belief Propagation and has convergence guarantees. Simulation and experimental results are presented that evaluate the performance of the outlier detection method and the inference algorithm on synthetic and real-world data.
