An improved serial method for mobile robot SLAM

Computational cost and real-time performance are important factors that many algorithms need to consider. For this purpose, this paper proposes an improved serial implementation strategy of mobile robot SLAM by combining Fast Fourier Transformation (FFT) and Iterative Closest Point (ICP) which is named FFT-ICP. We use FFT to localize coarsely and then use the results of FFT as the initial values of ICP to localize precisely. The experiments show that the method proposed here not only can speed up the operation, but also has higher precision through combining FFT and ICP. In addition, this method compromises the pros and cons of FFT and ICP. This technique is proven to be helpful for constructing an on-line SLAM system.

[1]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Yimin Zhou,et al.  An approach to restaurant service robot SLAM , 2016, 2016 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[3]  Paul Ozog,et al.  Real-time SLAM with piecewise-planar surface models and sparse 3D point clouds , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Andreas Nüchter,et al.  Parallelization of Scan Matching for Robotic 3D Mapping , 2007, EMCR.

[5]  Peng Wang,et al.  Gray-dynamic EKF for mobile robot SLAM in indoor environment , 2013, 2013 6th IEEE Conference on Robotics, Automation and Mechatronics (RAM).

[6]  Ryan M. Eustice,et al.  Fast LIDAR localization using multiresolution Gaussian mixture maps , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[7]  Hongjun Zhou,et al.  Localizing objects during robot SLAM in semi-dynamic environments , 2008, 2008 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.

[8]  Gang Yu,et al.  A new method for indoor low-cost mobile robot SLAM , 2017, 2017 IEEE International Conference on Information and Automation (ICIA).

[9]  Thierry Chateau,et al.  DCSLAM: A dynamically constrained real-time slam , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[10]  Roland Siegwart,et al.  A framework for multi-robot pose graph SLAM , 2016, 2016 IEEE International Conference on Real-time Computing and Robotics (RCAR).