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The SLAM problem is known to have a special property that when robotorientation is known, estimating the history of robot poses and featurelocations can be posed as a standard linear least squares problem. In thiswork, we develop a SLAM framework that uses relative feature-to-featuremeasurements to exploit this structural property of SLAM. Relative featuremeasurements are used to pose a linear estimation problem for pose-to-poseorientation constraints. This is followed by solving an iterative non-linearon-manifold optimization problem to compute the maximum likelihood estimate forrobot orientation given relative rotation constraints. Once the robotorientation is computed, we solve a linear problem for robot position and mapestimation. Our approach reduces the computational burden of non-linearoptimization by posing a smaller optimization problem as compared to standardgraph-based methods for feature-based SLAM. Further, empirical results show ourmethod avoids catastrophic failures that arise in existing methods due to usingodometery as an initial guess for non-linear optimization, while its accuracydegrades gracefully as sensor noise is increased. We demonstrate our methodthrough extensive simulations and comparisons with an existing state-of-the-artsolver.

RFM-SLAM: Exploiting Relative Feature Measurements to Separate Orientation and Position Estimation in SLAM