Abstract-The constraints of a low-cost consumer product pose a major challenge for designing a localization system. 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.
In the area of floor cleaning, Gutmann et al. REF propose an algorithm for simultaneously estimating a vector field induced by stationary signal sources in the environment as well as the robot pose in this vector field.
A constanttime algorithm for vector field SLAM using an exactly sparse extended information filter
In Proc. of Robotics: Science and Systems (RSS
Computer Science