RFM-SLAM: Exploiting Relative Feature Measurements to Separate Orientation and Position Estimation in SLAM
This work addresses efficiency and reliability challenges in SLAM for robotics, though it is incremental as it builds on known structural properties.
The authors tackled the computational burden and robustness issues in SLAM by developing a framework that separates orientation and position estimation using relative feature measurements, resulting in reduced optimization complexity and graceful degradation under noise while avoiding catastrophic failures.
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 burden 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.