Robust Incremental Smoothing and Mapping (riSAM)

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.

Authors

• Daniel McGann
• John G. Rogers
• Michael Kaess

Keywords

• Simultaneous localization and mapping
• Smoothing methods
• Automation
• Benchmark testing
• Optimization
• Robust Smoothing
• Incremental Smoothing
• Robust Method
• Smoothing Algorithm
• Least-squares
• Cost Function
• Control Parameters
• New Variables
• Conditional Independence
• Nonlinear Least Squares
• Linear Problem
• Kernel Methods
• Maximum A Posteriori
• Line Search
• Ground Truth Values
• Trust Region
• Inliers
• Batch Method
• Robust Estimation Method
• Quadratic Kernel
• Trust-region Algorithm