Marta Marrón

Novel HW Architecture Based on FPGAs Oriented to Solve the Eigen Problem

A hardware solution is presented to obtain the eigenvalues and eigenvectors of a real and symmetrical matrix using field-programmable gate arrays (FPGAs). Currently, this system is used to compute the eigenvalues and eigenvectors in covariance matrices for applications in digital image processing that make use of the principal component analysis (PCA) technique. The proposed solution in this paper is based on the Jacobi method, but in comparison with other related works, it presents a different architecture that remarkably improves execution time, while reducing the number of consumed resources of the FPGA.

Multi-Camera Sensor System for 3D Segmentation and Localization of Multiple Mobile Robots

This paper presents a method for obtaining the motion segmentation and 3D localization of multiple mobile robots in an intelligent space using a multi-camera sensor system. The set of calibrated and synchronized cameras are placed in fixed positions within the environment (intelligent space). The proposed algorithm for motion segmentation and 3D localization is based on the minimization of an objective function. This function includes information from all the cameras, and it does not rely on previous knowledge or invasive landmarks on board the robots. The proposed objective function depends on three groups of variables: the segmentation boundaries, the motion parameters and the depth. For the objective function minimization, we use a greedy iterative algorithm with three steps that, after initialization of segmentation boundaries and depth, are repeated until convergence.

Localization of Mobile Robots Using Odometry and an External Vision Sensor

This paper presents a sensor system for robot localization based on the information obtained from a single camera attached in a fixed place external to the robot. Our approach firstly obtains the 3D geometrical model of the robot based on the projection of its natural appearance in the camera while the robot performs an initialization trajectory. This paper proposes a structure-from-motion solution that uses the odometry sensors inside the robot as a metric reference. Secondly, an online localization method based on a sequential Bayesian inference is proposed, which uses the geometrical model of the robot as a link between image measurements and pose estimation. The online approach is resistant to hard occlusions and the experimental setup proposed in this paper shows its effectiveness in real situations. The proposed approach has many applications in both the industrial and service robot fields.

XPFCP: An Extended Particle Filter for Tracking Multiple and Dynamic Objects in Complex Environments

The work presented in this paper explores a new solution for tracking multiple and dynamic objects in complex environments. An extended particle filter (XPF) is used to implement a multimodal distribution that will represent the most probable estimation for each object position. A standard particle filter (PF) cannot be used with a variable number of obstacles, and some other solutions have been tested in different previous works, but most of them are very expensive in time and memory resources at least for a high number of obstacles to be tracked. The solution exposed here includes a clustering procedure that increases the robustness of the probabilistic process to adapt itself on-line to the variable number of clusters. The presented algorithm has been tested with sonar and stereovision measurements and some results included in the paper show the efficiency of the proposed work.

Comparing a Kalman Filter and a Particle Filter in a Multiple Objects Tracking Application

Two of the most important solutions in position estimation are compared, in this paper, in order to test their efficiency in a multi-tracking application in an unstructured and complex environment. A particle filter is extended and adapted with a clustering process in order to track a variable number of objects. The other approach is to use a Kalman filter with an association algorithm for each of the objects to track. Both algorithms are described in the paper and the results obtained with their real-time execution in the mentioned application are shown. Finally interesting conclusions extracted from this comparison are remarked at the end.

Modulation techniques comparison for three levels VSI converters

Nowadays, multilevel converters are the best option to work with high level power signals in medium voltage ranges. The aim of this article is to compare different continuous functions used to generate driving PWM signals in inverters: cosinusoidal, vectorial and cosinusoidal with zero-sequence functions. In this way, different MATLAB models have been developed to analyze and obtain important conclusions about supply voltage exploitation and load signal harmonic components for each proposed modulation technique. On the other hand, in this document the problem according to the unbalance of the NP voltage in NPC converters is also analyzed. In this way, NP voltage and current are shown and analyzed for each of the modulation methods presented.

Vision-based blind spot detection using optical flow

This paper describes a vision-based system for blind spot detection in intelligent vehicle applications. A camera is mounted in the lateral mirror of a car with the intention of visually detecting cars that can not be perceived by the vehicle driver since they are located in the so-called blind spot. The detection of cars in the blind spot is carried out using computer vision techniques, based on optical flow and data clustering, as described in the following lines.

XPFCP: an extended particle filter for tracking multiple and dynamic objects in complex environments

The work described in this paper explores a new solution for tracking multiple and dynamic objects in complex environments. An XPF (extended particle filter) is used to implement a multimodal distribution that represents the most probable estimation for each object position and velocity. A standard PF (particle filter) cannot be used with a variable number of obstacles; some other solutions have been tested in different previous works, but most of them require heavy computational resources at least for a high number of obstacles to be tracked. The solution described here includes a clustering procedure that increases the robustness of the probabilistic process in order to provide on-line adaptation to the variable number of clusters. The result is the XPFCP: extended particle filter with clustering process. The presented algorithm has been tested using stereovision measurements; the results included in the paper show the efficiency of the proposed system.

Localization and Geometric Reconstruction of Mobile Robots Using a Camera Ring

In this paper, a system capable of obtaining the 3-D pose of a mobile robot using a ring of calibrated cameras attached to the environment is proposed. The system robustly tracks point fiducials in the image plane of the set of cameras generated by the robots rigid shape in motion. Each fiducial is identified with a point belonging to a sparse 3-D geometrical model of the robots structure. Such a model allows direct pose estimation from image measurements, and it can easily be enriched at each iteration with new points as the robot motion evolves. The process is divided into an initialization step, where the structure of the robot is obtained, and an online step, which is solved using sequential Bayesian inference. The approach allows the proper modeling of uncertainty in measurements and estimations, and at the same time, it serves as a regularization step in pose estimation. The proposed system is verified using simulated and real data.

Low level controller for a POMDP based on WiFi observations

This paper shows the results of a low level controller that has been applied to an autonomous robotic system using a WiFi-based Partially Observable Markov Decision Process (POMDP). These observations provide a clue for global robot localization from the first iteration of the POMDP algorithm. Due to the noise channel of WiFi measures, it becomes necessary to make observations at a well known location within the environment. Therefore, a robust local navigator is needed in order to place the robot in an optimal position for making the observation. The system has been tested with two Pioneer 2AT robots in the premises of the Department of Electronics at the University of Alcala.