A Hybrid SLAM Method for Indoor Micro Aerial Vehicles

In this paper, a new simultaneous localization and mapping (SLAM) method for micro aerial vehicles (MAVs) is put forward. Its main contributions are the hybrid iterative closest points and normal distribution transform (ICP-NDT) point cloud registration algorithm as well as the extended Kalman filter (EKF) algorithm for data fusion and estimation based on the dynamic model of the quadrotor. In this method, a 2-dimensional (2D) lidar is used to obtain surrounding obstacle information in the region. Its data can be turned into displacement by the hybrid ICP-NDT registration algorithm, and projected to a planar occupancy grid submap by the imaging algorithm. The displacement can be integrated into EKF for data fusion with the other sensors to get the optimal position for the MAV, and the submap can be inserted into this optimal position for updating the map. As the process repeats, the map can establish. The presented algorithm is tested in two pieces of the real scene, and the MAV is capable of getting its position and establishing the map for the region. In these tests, the maps can reflect the planar features of the environment with satisfactory accuracy.

[1]  Bruno Steux,et al.  tinySLAM: A SLAM algorithm in less than 200 lines C-language program , 2010, 2010 11th International Conference on Control Automation Robotics & Vision.

[2]  Feng Lin,et al.  Design and implementation of an unmanned aerial vehicle for autonomous firefighting missions , 2016, 2016 12th IEEE International Conference on Control and Automation (ICCA).

[3]  Peter Biber,et al.  The normal distributions transform: a new approach to laser scan matching , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[4]  Sebastian Thrun,et al.  Integrating Grid-Based and Topological Maps for Mobile Robot Navigation , 1996, AAAI/IAAI, Vol. 2.

[5]  Wolfgang Hess,et al.  Real-time loop closure in 2D LIDAR SLAM , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[6]  Paul J. Besl,et al.  Method for registration of 3-D shapes , 1992, Other Conferences.

[7]  Kun Zhang,et al.  Robust autonomous flight and mission management for MAVs in GPS-denied environments , 2017, 2017 11th Asian Control Conference (ASCC).

[8]  Randall Smith,et al.  Estimating Uncertain Spatial Relationships in Robotics , 1987, Autonomous Robot Vehicles.

[9]  Stefan Kohlbrecher,et al.  A flexible and scalable SLAM system with full 3D motion estimation , 2011, 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics.

[10]  Wolfram Burgard,et al.  Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters , 2007, IEEE Transactions on Robotics.