Distributed map fusion with sporadic updates for large domains

Simultaneous localization and mapping (SLAM) algorithms allow a single robot to reduce the effects of drifting sensor biases while exploring unknown, GPS-denied environments. To reduce exploration time, a team of robots can build smaller maps in parallel and perform map fusion. Most map fusion techniques require a known relative transformation between coordinate frames. Other techniques rely on inter-robot detections to estimate an initial transformation. However, large environments with sparse robot coverage may necessitate alternative techniques when robots are in communication, but not, sensor range. In this paper, we address the map fusion problem with unknown relative transformations between robot pairs. We use the probabilistic hypothesis density (PHD) SLAM algorithm to track features within a static, simulated environment and propose two techniques for distributed map matching: a RANSAC based congruent triangle matching algorithm and an earth mover's distance (EMD) based assignment algorithm.