RSLAM: A System for Large-Scale Mapping in Constant-Time Using Stereo

  • Authors:

  • Christopher Mei;Gabe Sibley;Mark Cummins;Paul Newman;Ian Reid

  • Affiliations:

  • Robotics Research Group, Department of Engineering Science, Oxford University, Oxford, UK OX1 3PJ;Robotics Research Group, Department of Engineering Science, Oxford University, Oxford, UK OX1 3PJ;Robotics Research Group, Department of Engineering Science, Oxford University, Oxford, UK OX1 3PJ;Robotics Research Group, Department of Engineering Science, Oxford University, Oxford, UK OX1 3PJ;Robotics Research Group, Department of Engineering Science, Oxford University, Oxford, UK OX1 3PJ

  • Venue:
  • International Journal of Computer Vision
  • Year:
  • 2011

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Abstract

Large scale exploration of the environment requires a constant time estimation engine. Bundle adjustment or pose relaxation do not fulfil these requirements as the number of parameters to solve grows with the size of the environment. We describe a relative simultaneous localisation and mapping system (RSLAM) for the constant-time estimation of structure and motion using a binocular stereo camera system as the sole sensor. Achieving robustness in the presence of difficult and changing lighting conditions and rapid motion requires careful engineering of the visual processing, and we describe a number of innovations which we show lead to high accuracy and robustness. In order to achieve real-time performance without placing severe limits on the size of the map that can be built, we use a topo-metric representation in terms of a sequence of relative locations. When combined with fast and reliable loop-closing, we mitigate the drift to obtain highly accurate global position estimates without any global minimisation. We discuss some of the issues that arise from using a relative representation, and evaluate our system on long sequences processed at a constant 30---45 Hz, obtaining precisions down to a few meters over distances of a few kilometres.