Title: Mobile Robot Localization Based on Multi-Sensor Model for Assistance to Displacement of People with Reduce Mobility

Issue Number: Vol. 7, No. 1
Year of Publication: March - 2017
Page Numbers: 14-17
Authors: Wassila Meddeber, Youcef Touati, Arab Ali-Cherif
Journal Name: International Journal of New Computer Architectures and their Applications (IJNCAA)
- Hong Kong
DOI:  http://dx.doi.org/10.17781/P002286


This paper deals multi-sensor data fusion problems for mobile robot localization. In this context, we have proposed a Kalman Particle Kernel Filter (KPKF), which is based on a hybrid Bayesian filter, combining both extended Kalman and particle filters. The KPKF filter using a Gaussian mixture in which each component has a small covariance matrix. The Kalman correction updates the weights in order to bring particles back into the most probable space area. The KPKF combines the advantages of the regularized particulate filter in terms of robustness and the extended Kalman filter in terms of precision. This method can be applied for non-linear and multimodal environment and can improve localization performances. The proposed approach is implemented on a LIASD-Wheelchair experimental platform.