Deploying artificial landmarks to foster data association in simultaneous localization and mapping

Data association is an essential problem in simultaneous localization and mapping. It is hard to solve correctly, especially in ambiguous environments. We consider a scenario where the robot can ease the data association problem by deploying a limited number of uniquely identifiable artificial landmarks along its path and use them afterwards as fixed anchors. Obviously, the choice of the positions where the robot should drop these markers is crucial as poor choices might prevent the robot from establishing accurate data associations. In this paper, we present a novel approach for learning when to drop the landmarks so as to optimize the data association performance. We use Monte Carlo reinforcement learning for computing an optimal policy and apply a statistical convergence test to decide if the policy is converged and the learning process can be stopped. Extensive experiments also carried out with a real robot demonstrate that the data association performance using landmarks deployed according to our learned policies is significantly higher compared to other strategies.