Exactly Sparse Delayed-State Filters for View-Based SLAM

  • Authors:

  • R. M. Eustice;H. Singh;J. J. Leonard

  • Affiliations:

  • Joint Program in Oceanogr. Eng., Massachusetts Inst. of Technol., Cambridge, MA;-;-

  • Venue:
  • IEEE Transactions on Robotics
  • Year:
  • 2006

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Abstract

This paper reports the novel insight that the simultaneous localization and mapping (SLAM) information matrix is exactly sparse in a delayed-state framework. Such a framework is used in view-based representations of the environment that rely upon scan-matching raw sensor data to obtain virtual observations of robot motion with respect to a place it has previously been. The exact sparseness of the delayed-state information matrix is in contrast to other recent feature-based SLAM information algorithms, such as sparse extended information filter or thin junction-tree filter, since these methods have to make approximations in order to force the feature-based SLAM information matrix to be sparse. The benefit of the exact sparsity of the delayed-state framework is that it allows one to take advantage of the information space parameterization without incurring any sparse approximation error. Therefore, it can produce equivalent results to the full-covariance solution. The approach is validated experimentally using monocular imagery for two datasets: a test-tank experiment with ground truth, and a remotely operated vehicle survey of the RMS Titanic