Pairwise Consistent Measurement Set Maximization for Robust Multi-robot Map Merging

Abstract

This paper reports on a method for robust selection of inter-map loop closures in multi-robot simultaneous localization and mapping (SLAM). Existing robust SLAM methods assume a good initialization or an odometry backbone to classify inlier and outlier loop closures. In the multi-robot case, these assumptions do not always hold. This paper presents an algorithm called Pairwise Consistency Maximization (PCM) that estimates the largest pairwise internally consistent set of measurements. Finding the largest pairwise internally consistent set can be transformed into an instance of the maximum clique problem from graph theory, and by leveraging the associated literature it can be solved in real time. This paper evaluates how well PCM approximates the combinatorial gold standard using simulated data. It also evaluates the performance of PCM on synthetic and real-world data sets in comparison with DCS, SCGP, and RANSAC, and shows that PCM significantly outperforms these methods.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2017
Accession Number
AD1172568

Entities

People

  • Derrick Dominic
  • Joshua G. Mangelson
  • Ram Vasudevan
  • Ryan M. Eustice

Organizations

  • University of Michigan

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Anomaly Detection
  • Automation
  • Autonomous Underwater Vehicles
  • Cartography
  • Change Detection
  • Computer Vision
  • Consistency
  • Covariance
  • Data Sets
  • Detection
  • Errors
  • Graph Theory
  • Maps
  • Measurement
  • Robotics
  • Simultaneous Localization And Mapping
  • Vehicles

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Neural Network Machine Learning.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Autonomy