GPU accelerated graph SLAM and occupancy voxel based ICP for encoder-free mobile robots

Learning a map of an unknown environment and localising a robot in it is a common problem in robotics, with solutions usually requiring an estimate of the robot's motion. In scenarios such as Urban Search and Rescue, motion encoders can be highly inaccurate, and weight and battery requirements often limit computing power. We have developed a GPU based algorithm using Iterative Closest Point position tracking and Graph SLAM that can accurately generate a map of an unknown environment without the need for motion encoders and requiring minimal computational resources. The algorithm is able to correct for drift in the position tracking by rapidly identifying loops and optimising the map. We present a method for refining the existing map when revisiting areas to increase the accuracy of the existing map and bound the run-time to the size of the environment.