SLAM with salient line feature extraction in indoor environments

This paper presents a simultaneous localization and mapping (SLAM) of a large indoor environment using Rao-Blackwellized particle filter (RBPF) along with line segments as the landmarks. To represent the environment as a compact form, we use only two end points of the line segment, reducing computational cost in modeling line uncertainty. With a modified scan point clustering method, the proposed adaptive iterative end point fitting (IEPF) plays an important role in estimating line parameters by taking a noisy scan point near end points into account. Thus, by line-segment matching the robot is localized well in a local frame. We also introduce an online global optimization of a map, which provides more consistent map by removing spurious lines and merging collinear lines. Each of our approaches is efficiently integrated into the proposed RBPF-SLAM framework. Experiments with well-known data set demonstrate that the proposed method provides a reliable SLAM performance along with a compact map representation.