Localización y mapeo simultáneo (SLAM) utilizando un sensor de profundidad por infrarrojo

El propósito de este proyecto es diseñar y desarrollar un robot que implemente una solución al problema de SLAM (Simultaneous Localization And Mapping), utilizando una cámara y un sensor de profundidad por infrarrojo (Kinect). Para este fin se dividió el proyecto en tres etapas: a) robot: estructura con notebook y Kinect; b) software de procesamiento de imágenes; c) Mapeo y localización.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Localización y mapeo simultáneo (SLAM) utilizando un sensor de profundidad por infrarrojo

El propósito de este proyecto es diseñar y desarrollar un robot que implemente una solución al problema de SLAM (Simultaneous Localization And Mapping), utilizando una cámara y un sensor de profundidad por infrarrojo (Kinect). Para este fin se dividió el proyecto en tres etapas: a) robot: estructura con notebook y Kinect; b) software de procesamiento de imágenes; c) Mapeo y localización.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Localización y mapeo simultáneo (SLAM) utilizando un sensor de profundidad por infrarrojo

El propósito de este proyecto es diseñar y desarrollar un robot que implemente una solución al problema de SLAM (Simultaneous Localization And Mapping), utilizando una cámara y un sensor de profundidad por infrarrojo (Kinect). Para este fin se dividió el proyecto en tres etapas: a) robot: estructura con notebook y Kinect; b) software de procesamiento de imágenes; c) Mapeo y localización.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Evaluation of SLAM algorithms for Search and Rescue applications

This research investigates three SLAM algorithms on a low-cost mobile robot and finds the algorithms’ performance through a set of experiments including different types of ground surfaces

An Analytical Approach for Comparing Linearization Methods in EKF and UKF

The transformation of the mean and variance of a normally distributed random variable was considered through three different nonlinear functions: sin(x), cos(x), and xk, where k is a positive integer. The true mean and variance of the random variable after these transformations is theoretically derived within, and verified with respect to Monte Carlo experiments. These statistics are used as a reference in order to compare the accuracy of two different linearization techniques: analytical linearization used in the Extended Kalman Filter (EKF) and statistical linearization used in the Unscented Kalman Filter (UKF). This comparison demonstrated the advantage of using the unscented transformation in estimating the mean after transforming through each of the considered nonlinear functions. However, the variance estimation led to mixed results in terms of which linearization technique provided the best performance. As an additional analysis, the unscented transformation was evaluated with respect to its primary scaling parameter. A nonlinear filtering example is presented to demonstrate the usefulness of the theoretically derived results

An Analytical Approach for Comparing Linearization Methods in EKF and UKF

The transformation of the mean and variance of a normally distributed random variable was considered through three different nonlinear functions: sin(x), cos(x), and xk, where k is a positive integer. The true mean and variance of the random variable after these transformations is theoretically derived within, and verified with respect to Monte Carlo experiments. These statistics are used as a reference in order to compare the accuracy of two different linearization techniques: analytical linearization used in the Extended Kalman Filter (EKF) and statistical linearization used in the Unscented Kalman Filter (UKF). This comparison demonstrated the advantage of using the unscented transformation in estimating the mean after transforming through each of the considered nonlinear functions. However, the variance estimation led to mixed results in terms of which linearization technique provided the best performance. As an additional analysis, the unscented transformation was evaluated with respect to its primary scaling parameter. A nonlinear filtering example is presented to demonstrate the usefulness of the theoretically derived results

A Novel Approach To Intelligent Navigation Of A Mobile Robot In A Dynamic And Cluttered Indoor Environment

The need and rationale for improved solutions to indoor robot navigation is increasingly driven by the influx of domestic and industrial mobile robots into the market. This research has developed and implemented a novel navigation technique for a mobile robot operating in a cluttered and dynamic indoor environment. It divides the indoor navigation problem into three distinct but interrelated parts, namely, localization, mapping and path planning. The localization part has been addressed using dead-reckoning (odometry). A least squares numerical approach has been used to calibrate the odometer parameters to minimize the effect of systematic errors on the performance, and an intermittent resetting technique, which employs RFID tags placed at known locations in the indoor environment in conjunction with door-markers, has been developed and implemented to mitigate the errors remaining after the calibration. A mapping technique that employs a laser measurement sensor as the main exteroceptive sensor has been developed and implemented for building a binary occupancy grid map of the environment. A-r-Star pathfinder, a new path planning algorithm that is capable of high performance both in cluttered and sparse environments, has been developed and implemented. Its properties, challenges, and solutions to those challenges have also been highlighted in this research. An incremental version of the A-r-Star has been developed to handle dynamic environments. Simulation experiments highlighting properties and performance of the individual components have been developed and executed using MATLAB. A prototype world has been built using the WebotsTM robotic prototyping and 3-D simulation software. An integrated version of the system comprising the localization, mapping and path planning techniques has been executed in this prototype workspace to produce validation results

Magnetic Local Positioning System with Supplemental Magnetometer-Accelerometer Data Fusion

Geo-location and tracking technology, once confined to the industrial and military sectors, have been widely proliferated to the consumer world since early in the twenty-first century. The commoditization of Global Positioning System (GPS) and inertial measurement integrated circuits has made this possible, with devices small enough to fit in a cellular phone. However, GPS technology is not without its drawbacks: Its power use is high, and it can fail in smaller, obstructed spaces. Magnetic positioning, which exploits the magnetic field coupling between a set of transmitter beacon coils and a set of receiver coils, is an often overlooked, complementary technology that does not suffer from these problems. Magnetic positioning is strong where GPS is weak; however, it has some weaknesses of its own. Namely, it is subject to distortions due to metal objects in its immediate vicinity. In much of the prior art, these distortions are ignored or either statically measured and then corrected. This work presents a novel technique to dynamically correct for distorted fields. Specifically, a tri-axial magnetometer and a tri-axial accelerometer are integrated with the magnetic positioning system using a complementary Kalman filter. The end result resembles a tightly-coupled integrated GPS/inertial navigation system. The results achieved by this integrated magnetic positioning system prove the viability of the approach. The results are demonstrated in a real-world environment, where both strong, localized distortions and spatially broad distortions are corrected. In addition to the integrated magnetic position system, this work presents a novel scheme for calibrating the magnetic receiver; this technique is termed application domain calibration. In many real-world situations, low-level measurement and calibration will not be possible; therefore, this new technique uses the same set of demodulated and down-mixed data that is used by the magnetic positioning algorithms