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Our contributions A spectral clustering algorithm that can decide: “This point isn’t part of any cluster”

Edwin Olson, MIT Matthew Walter, MIT John Leonard, MIT Seth Teller, MIT http://cgr.csail.mit.edu. Single Cluster Graph Partitioning for Robotics Applications. Our contributions A spectral clustering algorithm that can decide: “This point isn’t part of any cluster”

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Our contributions A spectral clustering algorithm that can decide: “This point isn’t part of any cluster”

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  1. Edwin Olson, MITMatthew Walter, MITJohn Leonard, MITSeth Teller, MIT http://cgr.csail.mit.edu Single Cluster Graph Partitioningfor Robotics Applications • Our contributions • A spectral clustering algorithm that can decide: “This point isn’t part of any cluster” • Useful for real-world data sets with noise and mistakes • We extend clustering to non-square/non-symmetric matrices • Prior work • Ncuts, MinMax cuts… • No outlier handling; assumes all points belong to a logical cluster • Basic Idea • Find a set of points which are maximally mutually consistent. NCUT SCGP

  2. Edwin Olson, MITMatthew Walter, MITJohn Leonard, MITSeth Teller, MIT http://cgr.csail.mit.edu Single Cluster Graph Partitioningfor Robotics Applications • Outlier Rejection • Inputs: Noisy measurements • Outputs: Inliers • Data Association • Inputs: data-association hypotheses • Outputs: self-consistent data-association hypotheses • Feature Detection/Estimation • Inputs: points and proposed models • Outputs: self-consistent points and models • Lower error and faster than RANSAC Multi-track sonar range data No Outlier Rejection Outlier Rejection (SCGP) Observations Map Data Associations Line detection and extraction

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