Multi-robot teams show exceptional promise across applications like Search-and-Rescue, disaster-response, agriculture, forestry, and scientific exploration due to their ability to go where humans cannot, parallelize activity, operate robustly to failures, and expand capabilities beyond that of an individual robot. Collaborative Simultaneous Localization and Mapping (C-SLAM) is a fundamental capability for these multi-robot teams as it is required for them to plan, navigate, and, in turn, achieve their mission goals. A key component of the C-SLAM system is the back-end algorithm responsible for estimating the state of the robot team from their distributed, noisy measurements. However, existing C-SLAM back-end algorithms struggle to handle the practical conditions experienced by multi-robot teams deployed in the real-world. During real-world deployments multi-robot teams require C-SLAM back-end algorithms that are — 1) online to update estimates as new information is gathered, 2) robust to outlier data that we expect due to perceptual aliasing, 3) resilient to sparse and unreliable communication networks, 4) provide accurate and consistent solutions to the robot team, and 5) are scalable to large multi-robot teams we expect in future applications. In this thesis we propose a C-SLAM back-end algorithm that achieves all of these goals. We first explore Consensus Alternating Direction Method of Multipliers (C-ADMM) as a theoretical basis for C-SLAM algorithms. We then explore how to extend this base method to address the practicalities of real-world operation. Along the way we develop a novel incremental SLAM algorithm that addresses outlier measurements incrementally and in real-time. We complete these extensions with riMESA — a novel robust, incremental, distributed C-SLAM back-end algorithm designed for real-world C-SLAM problems. Finally, we develop a suite of benchmark C-SLAM datasets based on real-world data that are used to test and validate the proposed algorithm.

Thesis Committee Members:

Michael Kaess, Chair

Sebastian Scherer

George Kantor

Tim Barfoot, University of Toronto