Link Graph and Feature Chain Based Robust Online SLAM for Fully Autonomous Mobile Robot Navigation System Using Sonar Sensors

Abstract

Abstract-Local localization of a fully autonomous mobile robot in a partial map is an important aspect from the view point of accurate map building and safe path planning. The problem of correcting the location of a robot in a partial map worsens when sonar sensors are used. When a mobile robot is exploring the environment autonomously, it is rare to get the consistent pair of features or readings from two different positions using sonar sensors. So the approaches, which rely on readings or features matching, are prone to fail without exhaustive mathematical calculations of sonar modeling and environment modeling. This paper introduces link graph based robust two step feature chain based localization for achieving online SLAM (Simultaneous Localization And Mapping) using sonar data only. Instead of relying completely on matching of feature to feature or point to point, our approach finds possible associations between features to localize. The link graph based approach removes many false associations enhancing the SLAM process. We also map features onto Occupancy Grid (OG) framework taking advantage of its dense representation of the world. Combining features onto OG overcomes many of its limitations such as the independence assumption between cells and provides for better modeling of the sonar providing more accurate maps.