Collaborative multi-vehicle localization and mapping in high clutter environments

Among today's robotics applications, exploration missions in dynamic, high clutter and uncertain environmental conditions is quite common. Autonomous multi-vehicle systems come in handy for such exploration missions since a team of autonomous vehicles can explore an environment more efficiently and reliably than a single autonomous vehicle (AV). In order to improve the navigation accuracy, especially in the absence of a priori feature maps, various simultaneous localization and mapping (SLAM) algorithms are widely used in such applications. As for multi-vehicle scenarios, collaborative multi-vehicle simultaneous localization and mapping algorithm (CSLAM) is an effective strategy. However use of multiple AVs poses additional scaling problems such as inter-vehicle map fusion, and data association which needs to be addressed. Although existing CSLAM algorithms are shown to perform quite adequately in simulations, their performance is much less to be desired in high clutter scenarios that is inevitable in actual environments. In this paper, we present an approach to improve the performance of a CSLAM algorithm in the presence of high clutter, by combining an effective clutter filter framework based on Random Finite Sets (RFS). The performance of the improved CSLAM algorithm is evaluated using simulations under varying clutter conditions.