AntSLAM: Global map optimization using swarm intelligence

The capability of solving the simultaneous localization and mapping (SLAM) problem is one of the fundamental tasks of mobile robots and many research has focused on this problem over the last decades. In this paper, the SLAM problem is considered as the problem of finding an optimal path through a tree resulting in minimum costs. For this purpose, we apply the Ant Colony Optimization meta-heuristic, which belongs to the class of ant algorithms. It has been successfully employed to solve the well known Traveling Salesman Problem with several thousands of cities. We use a simple scan matching technique for generating a rough pre-solution to the SLAM problem. The (inconsistent) map is partitioned into fragments. A new fragment is initialized as soon as the robot has moved several meters. We draw samples from Gaussian distributions representing alignments of consecutive fragments. The resulting set of samples is interpreted as a tree-like data structure with weights assigned to the edges. We use our own variant of an ant algorithm for finding the optimal path through the tree. Real-world experimental results demonstrate the characteristics of our method.