Straight-lines modelling using planar information for monocular SLAM

Abstract

This work proposes a SLAM (Simultaneous Localization And Mapping) solution based on an Extended Kalman Filter (EKF) in order to enable a robot to navigate along the environment using information from odometry and pre-existing lines on the floor. These lines are recognized by a Hough transform and are mapped into world measurements using a homography matrix. The prediction phase of the EKF is developed using an odometry model of the robot, and the updating makes use of the line parameters in Kalman equations without any intermediate stage for calculating the distance or the position. We show two experiments (indoor and outdoor) dealing with a real robot in order to validate the project.

Vision based persistent localization of a humanoid robot for locomotion tasks

“…While this approach is inspired for solving PnP in a fast and robust way, it requires the knowledge of the 3D structure of the navigating space. One alternative could be using intersections between straight lines on the floor (Santana and Medeiros, 2012); unfortunately, not all working spaces exhibit linear patterns useful for robust localization.…”