论文标题
强大的增量平滑和映射（RISAM）
Robust Incremental Smoothing and Mapping (riSAM)
论文作者
论文摘要
本文介绍了一种用于在线增量同时本地化和映射（SLAM）的强大优化方法。由于在存在感知混叠的情况下数据关联的NP - 固定度，因此可进行拖延（大约）数据关联方法将产生错误的测量。我们需要猛烈的后端，在达到在线效率限制的同时，在存在异常值的情况下，可以将其收敛到准确的解决方案。现有的强大大满贯方法要么对异常值保持敏感，要么对初始化越来越敏感，要么无法提供在线效率。我们提出了可靠的增量平滑和映射（RISAM）算法，这是一种基于渐变的非凸性的稳健后端优化器，用于增量大满贯。我们在基准测试数据集上证明了我们的算法可以达到在线效率，优于现有的在线方法，并匹配或改善现有的离线方法的性能。
This paper presents a method for robust optimization for online incremental Simultaneous Localization and Mapping (SLAM). Due to the NP-Hardness of data association in the presence of perceptual aliasing, tractable (approximate) approaches to data association will produce erroneous measurements. We require SLAM back-ends that can converge to accurate solutions in the presence of outlier measurements while meeting online efficiency constraints. Existing robust SLAM methods either remain sensitive to outliers, become increasingly sensitive to initialization, or fail to provide online efficiency. We present the robust incremental Smoothing and Mapping (riSAM) algorithm, a robust back-end optimizer for incremental SLAM based on Graduated Non-Convexity. We demonstrate on benchmarking datasets that our algorithm achieves online efficiency, outperforms existing online approaches, and matches or improves the performance of existing offline methods.
