Title | ||
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RFM-SLAM: Exploiting Relative Feature Measurements to Separate Orientation and Position Estimation in SLAM. |
Abstract | ||
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The SLAM problem is known to have a special property that when robot orientation is known, estimating the history of robot poses and feature locations can be posed as a standard linear least squares problem. In this work, we develop a SLAM framework that uses relative feature-to-feature measurements to exploit this structural property of SLAM. Relative feature measurements are used to pose a linear estimation problem for pose-to-pose orientation constraints. This is followed by solving an iterative non-linear on-manifold optimization problem to compute the maximum likelihood estimate for robot orientation given relative rotation constraints. Once the robot orientation is computed, we solve a linear problem for robot position and map estimation. Our approach reduces the computational complexity of non-linear optimization by posing a smaller optimization problem as compared to standard graph-based methods for feature-based SLAM. Further, empirical results show our method avoids catastrophic failures that arise in existing methods due to using odometery as an initial guess for non-linear optimization, while its accuracy degrades gracefully as sensor noise is increased. We demonstrate our method through extensive simulations and comparisons with an existing state-of-the-art solver. |
Year | DOI | Venue |
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2017 | 10.1109/ICRA.2017.7989746 | international conference on robotics and automation |
DocType | Volume | Citations |
Conference | abs/1609.05235 | 0 |
PageRank | References | Authors |
0.34 | 14 | 3 |
Name | Order | Citations | PageRank |
---|---|---|---|
saurav agarwal | 1 | 8 | 2.56 |
Vikram Shree | 2 | 0 | 2.37 |
S. Chakravorty | 3 | 127 | 25.20 |