A Computationally Efficient Method for Large-Scale Concurrent Mapping and Localization

  • John J. Leonard
  • Hans Jacob S. Feder


Decoupled stochastic mapping (DSM) is a computationally efficient approach to large-scale concurrent mapping and localization. DSM reduces the computational burden of conventional stochastic mapping by dividing the environment into multiple overlapping submap regions, each with its own stochastic map. Two new approximation techniques are utilized for transferring vehicle state information from one submap to another, yielding a constant-time algorithm whose memory requirements scale linearly with the size of the operating area. The performance of two different variations of the algorithm is demonstrated through simulations of environments with 110 and 1200 features. Experimental results are presented for an environment with 93 features using sonar data obtained in a 3 by 9 by 1 meter testing tank.


Mobile Robot Error Bound Extended Kalman Filter Survey Area Autonomous Underwater Vehicle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag London 2000

Authors and Affiliations

  • John J. Leonard
    • 1
  • Hans Jacob S. Feder
    • 2
  1. 1.Dept. of Ocean EngineeringMITCambridgeUSA
  2. 2.Dept. of Ocean EngineeringMITCambridgeUSA

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