A Hybrid Intelligent System for Robot Ego Motion Estimation with a 3D Camera

Villaverde, Iván; Graña, Manuel

doi:10.1007/978-3-540-87656-4_81

Cited by 4 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In early attempts Villaverde and Graña [17], a SelfOrganizing Map (SOM) Kohonen [7] was used. This approach, however, presented several strong shortcomings.…”

Section: Neural Gas Modulementioning

confidence: 99%

See 1 more Smart Citation

Neuro-evolutionary mobile robot egomotion estimation with a 3D ToF camera

Villaverde

Graña

2010

Neural Comput & Applic

Self Cite

View full text Add to dashboard Cite

An innovative neuro-evolutionary approach for mobile robot egomotion estimation with a 3D ToF camera is proposed. The system is composed of two main modules following a preprocessing step. The first module is a Neural Gas network that computes a Vector Quantization of the preprocessed camera 3D point cloud. The second module is an Evolution Strategy that estimates the robot motion parameters by performing a registration process, searching on the space of linear transformations, restricted to the translation and rotation, between the codebooks obtained for successive camera readings. The fitness function is the matching error between the predicted and the observed codebook corresponding to the next camera readings. In this paper, we report results of an implementation of this system tested on data from a real mobile robot, and provide several comparisons between our and other well-known registration algorithms.

show abstract

“…In early attempts Villaverde and Graña [17], a SelfOrganizing Map (SOM) Kohonen [7] was used. This approach, however, presented several strong shortcomings.…”

Section: Neural Gas Modulementioning

confidence: 99%

“…maximizes the number of points that have a correspondence in both point clouds). We have tested [17,16], the mean of the matching distances as the fitness function F(h i ):…”

Section: Fitness Functionmentioning

confidence: 99%