Fast Active SLAM for Accurate and Complete Coverage Mapping of Unknown Environments

  • Kruno LenacEmail author
  • Andrej Kitanov
  • Ivan Maurović
  • Marija Dakulović
  • Ivan Petrović
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 302)


In this paper, we present an active SLAM solution with an active loop closing component which is independent on exploration component and at the same time allows high accuracy robot’s pose estimation and complete environment mapping. Inputs to our SLAM algorithm are RGBD image from the Kinect sensor and odometry estimates obtained from inertial measurement unit and wheel encoders. SLAM is based on the exactly sparse delayed state filter for real-time estimation of robot’s trajectory, vision-based pose registration, and loop closing. The active component ensures that localization remains accurate over a long period of time by sending the robot to close loops if a criterion function satisfies the predefined value. Our criterion function depends on the number of states predicted without an update between predictions, information gained from loop closing and the sheer distance between the loop closing state location and the current robot location. Once a state in which a loop closure should occur is reached and an update is performed, the robot returns to its previous goals. Since the active component is independent on the exploration part, the SLAM solution described in this paper can easily be merged with any existing exploration algorithm and the only requirement is that the exploration algorithm is able to stop exploration at any time and continue the exploration after the loop closing was accomplished. In this paper, we propose an active SLAM integration with the 2D laser range finder-based exploration algorithm that ensures the complete coverage of a polygonal environment and therefore a detailed mapping. The developed Active SLAM solution was verified through experiments which demonstrated its capability to work in real-time and to consistently map polygonal environments.


Model Predictive Control Visual Odometry Topological Distance Exploration Algorithm Loop Closing 
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.



This work has been supported by the European Community Seventh Framework Programme under grant No. 285939 (ACROSS).

Supplementary material

Supplementary material 1 (avi 9882 KB)


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Kruno Lenac
    • 1
    Email author
  • Andrej Kitanov
    • 1
  • Ivan Maurović
    • 1
  • Marija Dakulović
    • 1
  • Ivan Petrović
    • 1
  1. 1.Faculty of Electrical Engineering and ComputingUniversity of ZagrebZagrebCroatia

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