A real-time fast incremental SLAM method for indoor navigation

Recently, numerous efficient approaches to simultaneously localization and mapping (SLAM) based on ground robots have been proposed. However, we may encounter difficulties when applying those algorithms to those systems with higher real-time requirements, such as micro aerial vehicles (MAVs). This paper presents a fast and effective solution to SLAM problems, enables a quadrotor to autonomously explore unknown indoor environments. We propose a probabilistic approach to estimate the position based on scan matching algorithm. The estimation uncertainty is computed by a cost function. Furthermore, Bayesian method is used to update the occupancy probability grid map, which provides an effective way to solve sensor uncertainty. Experimental results carried out by using a laser range sensor on a quadrotor platform in indoor environment show that the incremental SLAM strategy has a superior performance.

[1]  Zhengyou Zhang,et al.  Iterative point matching for registration of free-form curves and surfaces , 1994, International Journal of Computer Vision.

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

[3]  Eduardo Nebot,et al.  Localization and map building using laser range sensors in outdoor applications , 2000, J. Field Robotics.

[4]  Wolfram Burgard,et al.  An efficient fastSLAM algorithm for generating maps of large-scale cyclic environments from raw laser range measurements , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[5]  E. Nebot,et al.  Autonomous Navigation and Map building Using Laser Range Sensors in Outdoor Applications , 2000 .

[6]  Hugh F. Durrant-Whyte,et al.  Simultaneous localization and mapping: part I , 2006, IEEE Robotics & Automation Magazine.

[7]  Hans P. Moravec.  Sensor Fusion in Certainty Grids for Mobile Robots , 1988, AI Mag..

[8]  Hugh F. Durrant-Whyte,et al.  A computationally efficient solution to the simultaneous localisation and map building (SLAM) problem , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[9]  Gamini Dissanayake,et al.  Simultaneous localisation and map building using millimetre wave radar to extract natural features , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[10]  James J. Little,et al.  Vision-based SLAM using the Rao-Blackwellised Particle Filter , 2005 .

[11]  James J. Little,et al.  Design and analysis of a framework for real-time vision-based SLAM using Rao-Blackwellised particle filters , 2006, The 3rd Canadian Conference on Computer and Robot Vision (CRV'06).

[12]  Lindsay Kleeman,et al.  Laser scan matching in polar coordinates with application to SLAM , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Andrea Censi,et al.  An ICP variant using a point-to-line metric , 2008, 2008 IEEE International Conference on Robotics and Automation.

[14]  Edwin Olson,et al.  Real-time correlative scan matching , 2009, 2009 IEEE International Conference on Robotics and Automation.

[15]  Wolfram Burgard,et al.  Towards a navigation system for autonomous indoor flying , 2009, 2009 IEEE International Conference on Robotics and Automation.

[16]  Hugh Durrant-Whyte,et al.  Simultaneous localization and mapping (SLAM): part II , 2006 .