This paper presents a probabilistic framework for road geometry estimation using a millimetre wave radar. It aims at estimating the geometry of roads without assuming any particular infrastructure such as lane marks. It provides also the vehicle location with respect to the edges of the road. This system employs a radar sensor in view of its robustness to weather conditions such as fog, dust, rain and snow. The proposed approach is robust to noisy measurements since the radar target locations are modelled as Gaussian distributions. These observations are integrated into a Kalman Particle filter to estimate the posterior distribution of the parameters that best describe the geometry of the road. Experimental results using data acquired on a highway road are presented. The effectiveness of the proposed approach is demonstrated by a qualitative analysis of the results.

This paper presents a probabilistic framework for unmarked roads estimation using radar sensors.The algorithm models the sensor likelihood function as a Gaussian mixture model.This sensor likelihood is used in a Bayesian approach to estimate the road edges probability distribution.A particle filter is used as the fusion mechanism to obtain posterior estimates of the road's parameters.The main applications of the approach presented are autonomous navigation and driver assistance.The use of radar permits the system to work even under difficult environmental conditions.Experimental results with data acquired in a mine environment are presented.By using a GPS mounted on the test vehicle, the algorithm outcome is registered with a satellite image of the experimental place.The registration allows to perform a qualitative analysis of the algorithm results.The results show the effectiveness of the algorithm presented.

Several works deal with 3D data in SLAM problem but many of them are focused on short scale maps. In this paper, we propose a method that can be used for computing the 6DoF trajectory performed by a robot from the stereo images captured during a large scale trajectory. The method transforms robust 2D features extracted from the reference stereo images to the 3D space. These 3D features are then used for obtaining the correct robot movement. Both Sift and Surf methods for feature extraction have been used. Also, a comparison between our method and the results of the ICP algorithm have been performed. We have also made a study about errors in stereo cameras.

Comunicacion presentada en el X Workshop of Physical Agents, Caceres, 10-11 septiembre 2009.

This paper presents a system for egomotion estimation using a stereo head camera. The camera motion estimation is based on features tracked along a video sequence. The system also estimates the tridimensional geometry of the environment by fusing the visual information from multiple views. Furthermore, the paper presents comparisons between two different algorithms. The first one is by applying triangulation to 3D points. Motion estimation using 3D points suffers from the problem of nonisotropic noise due to the large uncertainty in depth estimation. To deal with this problem we present results with a second approach that works directly in the disparity space. Experimental results using a mobile platform are presented. The experiments cover long distances in urban-like environments with the presence of dynamic objects. The system presented is part of a bigger project involving autonomous navigation using vision only.

Shadows are useful for synthetic images in order to increase extrinsically reality in image generation. However, in natural images, object recognition and segmentation are often negatively affected by cast shadows. Since shadows are a physical phenomena observed in most natural scenes, we propose a fast and reliable procedure to detect and attenuate shadows effects based on color/brightness density. Detected shadows are attenuated by modifying locally brightness and color that have the same color/brightness density. Some color artifacts (false colors on shadows) produced by the acquisition devices have been detected and discussed, and it has been noticed that they may affect some of the classical shadow removal methods. Finally, some experimental results of the proposed shadow attenuation method in real images are presented and evaluated.

In this article a characterization method of electric motors is presented. This method uses stochastic processing and artificial neural networks of second order to determine if a motor has some failures by means of the characterization of the tests made to the motor and the signals analysis obtained from it. First the signal is filtered to eliminate the noise using a stochastic filter and an artificial neural network to characterize and classify the signal in order to use it in the future as a comparison method for the analysis of the motor. The method and results obtained from the test made to the motor are presented.

In this works we present the design and implementation of a self-tuning control using artificial neural networks of second order. The control is implemented as an embedded system through a digital signal process (DSP). This system uses a digital control of closed loop proportional, derivative and integral (PID) self-tuning by artificial neural networks. In this works furthermore is presented a practical application by controller the velocity angular of a motor.

This work presents the design and implementation of vehicular access control using radio frequency identification (RFID). This system controls the accesses of three different parking and also it checks the access of each driver. With this system is possible to monitor, administer and report all the accesses and departs in each parking, this information can be available on a Web site. To this end, a database is generated with the names of the authorized people for accessing at the parking. A code number is assigned to each person, which represents the transponder tag number.