Contour reconstruction using recursive smoothing splines - experimental validation

In this paper, a recursive smoothing spline approach for contour reconstruction is studied and evaluated. Periodic smoothing splines are used by a robot to approximate the contour of encountered obstacles in the environment. The splines are generated through minimizing a cost function subject to constraints imposed by a linear control system and accuracy is improved iteratively using a recursive spline algorithm. The filtering effect of the smoothing splines allows for usage of noisy sensor data and the method is robust to odometry drift. Experimental evaluation is performed for contour reconstruction of three objects using a SICK laser scanner mounted on a PowerBot from ActivMedia Robotics.

[1]  Xiaoming Hu,et al.  Multi-Robot Terrain Servoing with Proximity Sensors , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[2]  Andrew J. Davison,et al.  Real-time simultaneous localisation and mapping with a single camera , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[3]  M. Egerstedt,et al.  Optimal control, statistics and path planning , 2001 .

[4]  M. Egerstedt,et al.  Optimal smoothing spline curves and contour synthesis , 2005 .

[5]  Paul Newman,et al.  SLAM-Loop Closing with Visually Salient Features , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[6]  Wolfram Burgard,et al.  A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots , 1998, Machine Learning.

[7]  Clyde F. Martin,et al.  Control Theoretic Smoothing Splines are Approximate Linear Filters , 2004, Commun. Inf. Syst..

[8]  M. Egerstedt,et al.  Statistical Estimates for Generalized Splines , 2003 .

[9]  José A. Castellanos,et al.  Mobile Robot Localization and Map Building: A Multisensor Fusion Approach , 2000 .

[10]  Hugh F. Durrant-Whyte,et al.  A solution to the simultaneous localization and map building (SLAM) problem , 2001, IEEE Trans. Robotics Autom..

[11]  Paolo Pirjanian,et al.  A Visual Front-end for Simultaneous Localization and Mapping , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[12]  Amir Averbuch,et al.  Butterworth wavelet transforms derived from discrete interpolatory splines: recursive implementation , 2001, Signal Process..

[13]  S. Isotani,et al.  A recursive spline-based algorithm for sensor calibration design , 1994, Proceedings of IECON'94 - 20th Annual Conference of IEEE Industrial Electronics.

[14]  BurgardWolfram,et al.  A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots , 1998 .

[15]  José A. Castellanos,et al.  Mobile Robot Localization and Map Building , 1999 .

[16]  A. Jazwinski Stochastic Processes and Filtering Theory , 1970 .

[17]  Santosh Devasia,et al.  Optimal output-transitions for linear systems , 2003, Autom..

[18]  Giorgio Picci,et al.  On line path following by recursive spline updating , 1995, Proceedings of 1995 34th IEEE Conference on Decision and Control.

[19]  Clyde F. Martin,et al.  Localization and mapping using recursive smoothing splines , 2007, 2007 European Control Conference (ECC).

[20]  Stefano Soatto,et al.  Visual path following by recursive spline updating , 1997, Proceedings of the 36th IEEE Conference on Decision and Control.

[21]  Lorenzo Ntogramatzidis,et al.  A parametrization of the solutions of the finite-horizon LQ problem with general cost and boundary conditions , 2005, Autom..

[22]  Henrik I. Christensen,et al.  Graphical SLAM using vision and the measurement subspace , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[23]  George M. Siouris,et al.  Applied Optimal Control: Optimization, Estimation, and Control , 1979, IEEE Transactions on Systems, Man, and Cybernetics.