We present a novel framework for collaboration amongst a team of robots performing Pose Graph Optimization (PGO) that addresses two important challenges for multi-robot SLAM: that of enabling information exchange "on-demand" via active rendezvous, and that of rejecting outlier measurements with high probability. Our key insight is to exploit relative position data present in the communication channel between agents, as an independent measurement in PGO. We show that our algorithmic and experimental framework for integrating Channel State Information (CSI) over the communication channels, with multi-agent PGO, addresses the two open challenges of enabling information exchange and rejecting outliers. Our presented framework is distributed and applicable in low-lighting or featureless environments where traditional sensors often fail. We present extensive experimental results on actual robots showing both the use of active rendezvous resulting in error reduction by 6X as compared to randomly occurring rendezvous and the use of CSI observations providing a reduction in ground truth pose estimation errors of 32 . These results demonstrate the promise of using a combination of multi-robot coordination and CSI to address challenges in multi-agent localization and mapping -- providing an important step towards integrating communication as a novel sensor for SLAM tasks.
As a supplement to some of the challenges of passive estimation, active SLAM approaches attempt to incorporate estimation uncertainty into the control of agents. This can include monitoring the estimation divergence during exploration @cite_28 and estimating information-gain from exploration versus uncertainty reduction via repeated visitations @cite_4 @cite_27 using pose graphs. However, almost all active SLAM approaches rely on an explicit model of the environment.
@cite_28 @cite_27 @cite_4
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Autonomous exploration under uncertain robot location requires the robot to use active strategies to trade-off between the contrasting tasks of exploring the unknown scenario and satisfying given constraints on the admissible uncertainty in map estimation. The corresponding problem, namely active SLAM (Simultaneous Localization and Mapping) and exploration, has received a large attention from the robotic community for its relevance in mobile robotics applications. In this work we tackle the problem of active SLAM and exploration with Rao-Blackwellized Particle Filters. We propose an application of Kullback-Leibler divergence for the purpose of evaluating the particle-based SLAM posterior approximation. This metric is then applied in the definition of the expected information from a policy, which allows the robot to autonomously decide between exploration and place revisiting actions (i.e., loop closing). Extensive tests are performed in typical indoor and office environments and on well-known benchmarking scenarios belonging to SLAM literature, with the purpose of comparing the proposed approach with the state-of-the-art techniques and to evaluate the maturity of truly autonomous navigation systems based on particle filtering. We propose a novel method for robotic exploration that evaluates paths that minimize both the joint path and map entropy per meter traveled. The method uses Pose SLAM to update the path estimate, and grows an RRT* tree to generate the set of candidate paths. This action selection mechanism contrasts with previous appoaches in which the action set was built heuristically from a sparse set of candidate actions. The technique favorably compares agains the classical frontier-based exploration and other Active Pose SLAM methods in simulations in a common publicly available dataset.