Chapter

# Augmenting Sparse Laser Scans with Virtual Scans to Improve the Performance of Alignment Algorithms

In book: Cutting Edge Robotics 2010

Source: InTech

- Citations (20)
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**ABSTRACT:**The interest in the cognitive phenomena linked to mental imagery and to reasoning with mental images has sparked the development of numerous conceptual and computational models from different scientific backgrounds. These models serve to explain cognitive processes or to improve technical reasoning models. Our aim in this paper is to identify a num-ber of requirements that are essential for any computational model of mental imagery. This stocktaking of requirements is useful for the critical assessment of existing models of mental imagery as well as for improving future models. We assess three prevalent computational imagery models and conclude with an outlook on a next generation of imagery models that will implement the set of requirements. -
##### Conference Paper: Convergence analysis for extended Kalman filter based SLAM

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**ABSTRACT:**The main contribution of this paper is a theoretical analysis of the extended Kalman filter (EKF) based solution to the simultaneous localisation and mapping (SLAM) problem. The convergence properties for the general nonlinear two-dimensional SLAM are provided. The proofs clearly show that the robot orientation error has a significant effect on the limit and/or the lower bound of the uncertainty of the landmark location estimates. Furthermore, some insights to the performance of EKF SLAM and a theoretical analysis on the inconsistencies in EKF SLAM that have been recently observed are givenRobotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on; 02/2006 - [Show abstract] [Hide abstract]

**ABSTRACT:**Our goal is polygonal approximation of laser range data points obtained by a mobile robot. The proposed approach provides a precise estimation of the number of model components (line segments) and their initial parameters independent of their initial values. We use principles of perceptual grouping to evaluate the approximation quality obtained in each expectation maximization (EM) step. By evaluating EM approximation quality we are able to recognize a locally optimal solution, and modify the number of model components and their parameters. Consequently, EM can converge only to a globally optimal solution independent of the initial number of model components and their initial parametersProceedings of the 2006 IEEE International Conference on Robotics and Automation, ICRA 2006, May 15-19, 2006, Orlando, Florida, USA; 01/2006

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