A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Exploring artificial intelligence in the new millennium
A Hashing Strategy for Efficient k -Nearest Neighbors Computation
CGI '99 Proceedings of the International Conference on Computer Graphics
Practical Genetic Algorithms with CD-ROM
Practical Genetic Algorithms with CD-ROM
A review of recent range image registration methods with accuracy evaluation
Image and Vision Computing
A Hybrid Intelligent System for Robot Ego Motion Estimation with a 3D Camera
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
GPGPU implementation of growing neural gas: Application to 3D scene reconstruction
Journal of Parallel and Distributed Computing
3D Video Based Segmentation and Motion Estimation with Active Surface Evolution
Journal of Signal Processing Systems
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We propose an evolutionary approach for egomotion estimation with a 3D TOF camera. It is composed of two main modules plus a preprocessing step. The first module computes the Neural Gas (NG) approximation of the preprocessed camera 3D data. The second module is an Evolution Strategy which performs the task of estimating the motion parameters by searching on the space of linear transformations restricted to the translation and rotation, applied on the codevector sets obtained by the NG for successive camera readings. The fitness function is the matching error between the transformed last set of codevectors and the codevector set corresponding to the next camera readings. In this paper, we report new modifications and improvements of this system and provide several comparisons between our and other well known registration algorithms.