Abstract. We propose a new formulation for including orthogonal planar features as a global model into a linear SLAM approach based on
sequential Bayesian filtering. Previous planar SLAM algorithms estimate
the camera poses and multiple landmark planes in a pose graph optimization. However, since it is formulated as a high dimensional nonlinear optimization problem, there is no guarantee the algorithm will converge to
the global optimum. To overcome these limitations, we present a new
SLAM method that jointly estimates camera position and planar landmarks in the map within a linear Kalman filter framework. It is rotations
that make the SLAM problem highly nonlinear. Therefore, we solve for
the rotational motion of the camera using structural regularities in the
Manhattan world (MW), resulting in a linear SLAM formulation. We
test our algorithm on standard RGB-D benchmarks as well as additional
large indoor environments, demonstrating comparable performance to
other state-of-the-art SLAM methods without the use of expensive nonlinear optimization