The objective of this thesis is to present a mapping algorithm for autonomous navigation of a lawnmower based on the range and bearing information of a SICK laser. The Simultaneous Localization and Mapping (SLAM) algorithm is a tool that can be used to navigate unmanned vehicle in an unknown environment. The two-dimensional (2D) maps of the obstacles can be obtained if the laser scans the environment in a plane horizontal to the ground. However, the 2D map does not give information of the objects that are placed below the height of the laser. This makes it difficult for the lawn mower to navigate the field since one of the objects (flower bed) is placed below the height of the laser. The computational complexity of the SLAM also makes it difficult to implement the algorithm on a low cost lawn mower.
This thesis presents a mapping algorithm to map the environment that contains objects at a height below the laser. The localization of the unmanned vehicle is obtained from GPS (Global Positioning System) and IMU (Inertial Measurement Unit). The algorithm is simple and easy to implement as compared to the SLAM algorithm and was validated on the lawn mower. The position and dimensions of the flowerbed obtained using this algorithm closely matched the actual position from a reference point and dimensions of the flowerbed.