Trust But Verify: A Distributed Algorithm for MultiRobot Wireframe Exploration and Mapping This paper presents a novel distributed mapping algorithm for multiple resourceconstrained robots operating in a rectilinear 2D environment. The algorithm is built upon the sparse wireframe map representation and updating framework in 1. We propose an exploration strategy based on the labeling of the vertices in the wireframe map, combined with a mapmerging interrupt routine that is activated when robots enter into communication range with one another. The maps are not naively merged, but instead the receiving robot verifies the received information before it is assimilated by attempting to drive to the location where the other robot was when communication was established. The robots do not share a global coordinate frame, so prior to a merge the relative map alignment is determined. This is achieved using the random sample consensus (RANSAC) framework with a custom feature which leverages the structure inherent in the wireframe map representation. This results in a lower rate of falsepositive matches compared to another stateoftheart feature used in point cloud alignment, the 4point congruent set (4PCS). We show our feature to be more robust to falsepositive alignments, a common occurrence when attempting to align sparse structures such as wireframe maps. We present high fidelity simulation results in a ROSGazebo environment with lidarequipped TurtleBots1 to highlight the benefits of our algorithm.1The TurtleBot3 burger configuration www.turtlebot.com

Optimal Temporal Logic Planning for MultiRobot Systems in Uncertain Semantic Maps This paper addresses a multirobot motion planning problem in probabilistic maps obtained by semantic simultaneous localization and mapping (SLAM). The goal of the robots is to accomplish complex collaborative high level tasks captured by global temporal logic specifications in the presence of uncertainty in the workspace. Specifically, the robots operate in an unknown environment modeled as a semantic map determined by Gaussian distributions over landmark positions and arbitrary discrete distributions over landmark classes. We extend Linear Temporal Logic by including informationbased predicates allowing us to incorporate uncertainty and probabilistic satisfaction requirements directly into the task specification. We propose a new highly scalable samplingbased approach that synthesizes paths that satisfy the assigned task specification while minimizing a userspecified motion cost function. Finally, we show that the proposed algorithm is probabilistically complete, asymptotically optimal and supported by convergence rate bounds. We provide extensive simulation results that corroborate the theoretical analysis and show that the proposed algorithm can address largescale planning tasks.