Autonomous loop exploration and SLAM with fusion of advanced sonar and laser polar scan matching

SLAM is a well-studied technique for robots to build a map of environments while at the same time keeping track of their pose (position and orientation). However, SLAM does not provide motion control approaches for how the robot explores the environment. This paper presents an integrated approach to create a fully autonomous exploring and mapping robot. An Extended Kalman Filter (EKF)-SLAM approach is used to fuse Advanced Sonar measurements and the results of Laser Polar Scan Matching. The inclusion of Advanced Sonar data tackles the problem of map drifts in environments where lasers do not supply sufficient information, such as along a corridor. In addition, the proposed exploration strategy takes advantage of the characteristic of the Voronoi graph to enable the robot to strategically explore the environment in a loop-closing fashion and a safe manner. By revisiting areas to close loops as early as possible, the robot builds a more stable map incrementally while still reliably tracking its pose. Experimental results of the integrated approach are shown to demonstrate the paper's approach that provides real-time exploration of a mobile robot in an initially unknown real environment. Experimental map quality comparisons using exploration strategies show the benefits of early local loop-closing.