Distributed Consensus with Visual Perception in Multi-Robot Systems

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Intro -- Preface -- Contents -- Symbols -- 1 Introduction -- 1.1 Motivation -- 1.1.1 Multi-robot Systems -- 1.1.2 Distributed Consensus and Vision Sensors -- 1.2 Topics of the Book -- 1.3 Outline -- 2 Robotic Networks and the Consensus Problem -- 2.1 Introduction -- 2.2 Model of the Robotic Network -- 2.2.1 Definition of a Robot -- 2.2.2 Communication Model -- 2.3 Distributed Consensus and Linear Iterations -- 2.3.1 Max Consensus and Average Consensus -- 2.3.2 Definition of the Matrix Weights -- 2.4 Application of Linear Iterations and Consensus in Perception Problems -- 2.5 Discussion -- 3 The Data Association Problem -- 3.1 Introduction -- 3.2 Problem Description -- 3.2.1 Matching Between Two Robots -- 3.2.2 Centralized Matching Between n Robots -- 3.2.3 Matching Between n Robots with Limited Communications -- 3.3 Propagation of Matches and Detection of Inconsistencies -- 3.3.1 Example of Execution -- 3.4 Decentralized Resolution of Inconsistent Associations -- 3.4.1 Resolution Using the Maximum Error Cut -- 3.4.2 Resolution Based on Spanning Trees -- 3.4.3 Example of Execution -- 3.5 Experiments -- 3.5.1 Simulations -- 3.5.2 Experiments with Real Images -- 3.6 Discussion -- 4 D-RANSAC: Distributed Robust Consensus -- 4.1 Introduction -- 4.2 Distributed Averaging Using Homogeneous Coordinates -- 4.2.1 Averaging in Finite Time -- 4.2.2 A Distributed Primitive for Robot Counting -- 4.3 Outlier Detection with RANSAC -- 4.4 D-RANSAC: Distributed Robust Consensus Algorithm -- 4.4.1 Distributed Generation of Random Hypotheses -- 4.4.2 Distributed Voting of the Hypotheses -- 4.4.3 Consensus of the Inliers -- 4.4.4 Computation and Communication Analysis of the Algorithm -- 4.5 D-RANSAC Using Dynamic Voting -- 4.5.1 Conditions to Reach the Maximum Likelihood of the Inliers -- 4.6 Applications of D-RANSAC -- 4.6.1 Illustrative Example

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