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.

翻译：本文提出一种用于在线增量式同时定位与建图（SLAM）的鲁棒优化方法。由于在感知混淆存在时数据关联的NP难特性，可处理的（近似）数据关联方法会产生错误测量值。我们要求SLAM后端能够在存在离群测量值的情况下收敛到精确解，同时满足在线效率约束。现有的鲁棒SLAM方法要么仍对离群值敏感，要么对初始化变得敏感，要么无法保证在线效率。我们提出了鲁棒增量式平滑与建图（riSAM）算法，这是一种基于渐进非凸性的增量SLAM鲁棒后端优化器。通过在基准数据集上的实验表明，我们的算法实现了在线效率，优于现有在线方法，并且达到或提升了现有离线方法的性能。