Research group

Mobile Robot Algorithms Laboratory

SLAM Constructor for ROS

Project supervisor: Kirill Krinkin
Status: Active

Project domain

Simultaneous Localization and Mapping (SLAM) methods are essential for mobile robots which are supposed to act in an unknown environment. In spite of various algorithms have already been proposed, an algorithm that robustly solves the problem in general case and satisfies performance constraints is still a subject of research. Unfortunately, there is no publicly available framework that provides a common set of components in order to speed up SLAM research (frameworks and toolkits that simplify development of particular SLAM parts are not taken into account).

Project goals

  • creation of a framework that acts as a constructor of SLAM algorithms (a researcher is supposed to connect available components by himself and add necessary modifications);
  • implementation of full set of basic components that can be assembled into the most common (fundamental) SLAM algorithms;
  • creation of the infrastructure and tools for SLAM algorithms debugging and analysis;
  • creation of service for management SLAM datasets (converting, storing, providing etc);

Environment

All software we develop is supposed to be executed in Linux and Robot Operating System environment. (We did preliminary research wide spectrum of existing tools and environments like MTK, MRPT and others and have seen that ROS is most promising choice)

Roadmap

  • Support components for graph-based SLAM methods;
  • Implementation extra scan matchers for Laser based SLAMs
  • Support extended set of sensors and measurements (3D scans, monocular/stereo cameras)
  • Vergent stereo vision implementation
  • SLAM datasets service
  • ROS SLAM Testing Farm: (virtualized, container based environment, for semi-autonomous SLAM algorithms testing)

Participants

Publications

  • Intelligent Robots and Systems (IROS), 2017 IEEE/RSJ International Conference,
  • An. Filatov, Ar. Filatov, K. Krinkin, B. Chen, D. Molodan

    SLAM (Simultaneous Localization and mapping) is one of the most challenging problems for mobile platforms and there is a huge amount of modern SLAM algorithms. The choice of the algorithm that might be used in every particular problem requires prior knowledge about advantages and disadvantages of each algorithm. This paper presents the approach for comparison of SLAM algorithms that allows to find the most accurate one. The accent of research is made on 2D SLAM algorithms and the focus of analysis is 2D map that is built after algorithm performance. Three metrics for evaluation of maps are presented in this paper