鲁棒增量平滑与建图（riSAM）
Robust Incremental Smoothing and Mapping (riSAM)
AI总结：
针对感知混叠下SLAM数据关联易产生外点、现有方法难兼顾鲁棒性与在线效率的问题，提出基于渐进非凸性的riSAM增量SLAM后端优化算法，在基准数据集上实现优于现有在线方法、匹配或超越离线方法的性能。
摘要：
本文提出一种面向在线增量同时定位与建图（SLAM）的鲁棒优化方法。由于存在感知混叠时数据关联具有NP难特性，易处理的（近似）数据关联方法会产生错误测量。我们需要SLAM后端能在存在外点测量的情况下收敛到精确解，同时满足在线效率约束。现有鲁棒SLAM方法要么仍对外点敏感，要么对初始化越来越敏感，要么无法保证在线效率。我们提出鲁棒增量平滑与建图（riSAM）算法，这是一种基于渐进非凸性（Graduated Non-Convexity）的增量SLAM鲁棒后端优化器。我们在基准数据集上验证表明，该算法具备在线效率，性能优于现有在线方法，且达到或优于现有离线方法的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
