ICRA 2012 Paper Abstract


Paper TuC05.1

Cunningham, Alexander (Georgia Institute of Technology), Wurm, Kai M. (University of Freiburg), Burgard, Wolfram (University of Freiburg), Dellaert, Frank (Georgia Institute of Technology)

Fully Distributed Scalable Smoothing and Mapping with Robust Multi-Robot Data Association

Scheduled for presentation during the Regular Session "Multi-Robot Systems 1" (TuC05), Tuesday, May 15, 2012, 14:30−14:45, Meeting Room 5 (Ska)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on October 15, 2018

Keywords SLAM, Distributed Robot Systems, Sensor Networks


In this paper we focus on the multi-robot perception problem, and present an experimentally validated end-to-end multi-robot mapping framework, enabling individual robots in a team to see beyond their individual sensor horizons. The inference part of our system is the DDF-SAM algorithm, which provides a decentralized communication and inference scheme, but did not address the crucial issue of data association. One key contribution is a novel, RANSAC-based, approach for performing the between-robot data associations and initialization of relative frames of reference. We demonstrate this system with both data collected from real robot experiments, as well as in a large scale simulated experiment demonstrating the scalability of the proposed approach.



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