An Autonomous Simultaneous Localization and Mapping Walker for Indoor Navigation

Abstract

Walkers have been used to help the elderly and individuals with movement disorders as an assistive and rehabilitation tool. This study presents a smart walker, a system which guides the users to navigate in an indoor environment. The Walker can be controlled by voice commands to create location markers and navigate the user while avoiding obstacles. We evaluated three localization implementations, namely, Adaptive Monte Carlo Localization (AMCL), Gmapping and Hector_Slam for this system and compared their navigation accuracy with an ideal path. We collected the data on the paths of AMCL, Gmapping and Hector_Slam and applied statistical tests on the data. The results show that AMCL achieves the lowest mean absolute error while navigating to its goal with an error of 2.15% over the path distance, as compared to Gmapping and Hector in this implementation.