Vector field SLAM

— Designing a localization system for a low-cost robotic consumer product poses a major challenge. In previous work, we introduced Vector Field SLAM [5], a system for simultaneously estimating robot pose and a vector field induced by stationary signal sources present in the environment. In this paper we show how this method can be realized on a low-cost embedded processing unit by applying the concepts of the Exactly Sparse Extended Information Filter [15]. By restricting the set of active features to the 4 nodes of the current cell, the size of the map becomes linear in the area explored by the robot while the time for updating the state can be held constant under certain approximations. We report results from running our method on an ARM 7 embedded board with 64 kByte RAM controlling a Roomba 510 vacuum cleaner in a standard test environment.

• Type: Journal
• Year: 2010
• Where: ICRA
• Authors: Jens-Steffen Gutmann, Gabriel Brisson, Ethan Eade, Philip Fong, Mario E. Munich
• Keywords: ICRA 2010, Low-cost Embedded Processing, Low-cost Robotic Consumer, Robotics, Vector Field SLAM