Vision SLAM in the Measurement Subspace

In this paper we describe an approach to feature representation for simultaneous localization and mapping, SLAM. It is a general representation for features that addresses symmetries and constraints in the feature coordinates. Furthermore, the representation allows for the features to be added to the map with partial initialization. This is an important property when using oriented vision features where angle information can be used before their full pose is known. The number of the dimensions for a feature can grow with time as more information is acquired. At the same time as the special properties of each type of feature are accounted for, the commonalities of all map features are also exploited to allow SLAM algorithms to be interchanged as well as choice of sensors and features. In other words the SLAM implementation need not be changed at all when changing sensors and features and vice versa. Experimental results both with vision and range data and combinations thereof are presented.

[1]  Sebastian Thrun,et al.  FastSLAM: a factored solution to the simultaneous localization and mapping problem , 2002, AAAI/IAAI.

[2]  J. M. M. Montiel,et al.  The SPmap: a probabilistic framework for simultaneous localization and map building , 1999, IEEE Trans. Robotics Autom..

[3]  Darius Burschka,et al.  V-GPS(SLAM): vision-based inertial system for mobile robots , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[4]  Henrik I. Christensen,et al.  Graphical SLAM - a self-correcting map , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[5]  James J. Little,et al.  Mobile Robot Localization and Mapping with Uncertainty using Scale-Invariant Visual Landmarks , 2002, Int. J. Robotics Res..

[6]  Evangelos E. Milios,et al.  Optimal global pose estimation for consistent sensor data registration , 1995, Proceedings of 1995 IEEE International Conference on Robotics and Automation.

[7]  Alberto Elfes,et al.  Using occupancy grids for mobile robot perception and navigation , 1989, Computer.

[8]  John J. Leonard,et al.  Directed Sonar Sensing for Mobile Robot Navigation , 1992 .

[9]  Eduardo Mario Nebot,et al.  Optimization of the simultaneous localization and map-building algorithm for real-time implementation , 2001, IEEE Trans. Robotics Autom..

[10]  Sebastian Thrun,et al.  Results for outdoor-SLAM using sparse extended information filters , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).