Simultaneous localization and mapping (SLAM) with an autonomous robot

Abstract

Simultaneous localization and mapping (SLAM) is a problem in which an autonomous robot is required to initiate in an unknown location within an unknown map and incrementally build a map of its environment while also using sensory data and the map to find its location with very minimal errors. We present a solution for SLAM with an autonomous differential drive robot in an indoor environment. We extract data from sonars and analyze it to construct a map of the explored area using occupancy grid mapping. In addition, relative positioning approaches provide location using an inertial measurement unit (IMU) and wheel encoders. Localization and mapping are essential tasks for an autonomous robot's navigation or exploration without a prior map and the system is based on discrete step-wise or event modeling which guides its navigation. The systems result is compared for accuracy against an actual map.