The 7th IFAC Symposium on Intelligent Autonomous Vehicles 2010

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SLAM base Selective Submap Joining for the Victoria Park Dataset

Josep Aulinas
Computer Vision and Robotics group (ViCoRob), University of Girona
Spain

Xavier Lladó
Computer Vision and Robotics group (ViCoRob), University of Girona
Spain

Joaquim Salvi
Computer Vision and Robotics group (ViCoRob), University of Girona
Spain

Yvan R. Petillot
Ocean Systems Lab, Heriot Watt University
United Kingdom

Abstract:
One of the main drawbacks of current SLAM algorithms is that they do not result in consistent maps of large areas, mainly because the uncertainties increase with the scenario. In addition, as the map size grows the computational costs increase, making SLAM solutions unsuitable for on-line applications. The use of local maps has been demonstrated to be useful in these situations, reducing computational cost and improving map consistency. Following this idea, this paper proposes a technique based on using independent local maps together with a global stochastic map. The global level contains the relative transformations between local maps, which are updated once a new loop is detected. In addition, the information within the local maps is also corrected. Thus, maps sharing a high number of features are updated through fusion and the correlation between landmarks and vehicle is maintained. Results on synthetic data and on the Victoria Park Dataset show that our approach is able to consistently map large areas and the computational costs are lower.

 

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