Robust Incremental Smoothing and Mapping - Robotics Institute Carnegie Mellon University
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PhD Speaking Qualifier

December

1
Thu
Daniel McGann PhD Student Robotics Institute,
Carnegie Mellon University
Thursday, December 1
12:00 pm to 1:00 pm
NSH 3001
Robust Incremental Smoothing and Mapping

Abstract:
In this work we present 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.

Committee:
Michael Kaess (Chair)
David Wettergreen
George Kantor
Sudharshan Suresh