  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.
