Showing papers by "Sauro Longhi published in 2005"

A Bayesian approach to the Hough transform for line detection

Abstract: This paper explains how to associate a rigorous probability value to the main straight line features extracted from a digital image. A Bayesian approach to the Hough Transform (HT) is considered. Under general conditions, it is shown that a probability measure is associated to each line extracted from the HT. The proposed method increments the HT accumulator in a probabilistic way: first calculating the uncertainty of each edge point in the image and then using a Bayesian probabilistic scheme for fusing the probability of each edge point and calculating the line feature probability.

Localization of mobile robots: Development and comparative evaluation of algorithms based on odometric and inertial sensors

Abstract: An autonomous mobile robot must be able to elaborate the measures provided by the sensor equipment to localize itself with respect to a coordinate system. The precision of the location estimate depends on the sensor accuracy and on the reliability of the measure processing algorithm. The purpose of this article is to propose a low cost positioning system using internal sensors like odometers and optical fiber gyroscopes. Three simple localization algorithms based on different sensor data processing procedures are presented. Two of them operate in a deterministic framework, the third operates in a stochastic framework where the uncertainty is induced by sensing and unmodeled robot dynamics. The performance of the proposed localization algorithms are tested through a wide set of laboratory experiments and compared in terms of localization accuracy and computational cost. © 2005 Wiley Periodicals, Inc.

Improving the Robustness Properties of Robot Localization Procedures with Respect to Environment Features Uncertainties

Abstract: In this paper the localization and environment feature estimation problems are formulated in a stochastic setting and an Extended Kalman Filtering (EKF) approach is proposed for the integration of odometric, gyroscope and sonar measures. As gyroscopic measures are much more reliable than the other ones, the localization algorithm gives rise to a nearly singular EKF. This problem is dealt with defining a lower order non singular EKF.

Improved set-points tracking of remotely operated underwater vehicles through a supervised PID control scheme

Abstract: This paper considers the position control problem of an underwater vehicle used in the exploitation of combustible gas deposits at great water depths. The use of a single, time invariant PID controller may result in an unsatisfactory set-point tracking. To cope with this problem, a globally non-linear regulator given by the connection of a supervisor with a finite family of properly chosen PID controllers is here proposed. Each PID is designed to obtain a specific desired feature of the step response and the supervisor governs the switching among them to obtain an overall time-varying control law yielding a globally satisfactory output behaviour. The switching logic is driven by the numerical values assumed by some suitably defined cost functionals of output prediction errors. The performance of the switched controller so obtained is evaluated by numerical simulations.

Localization of mobile robots: Development and comparative evaluation of algorithms based on odometric and inertial sensors: Research Articles

Abstract: An autonomous mobile robot must be able to elaborate the measures provided by the sensor equipment to localize itself with respect to a coordinate system. The precision of the location estimate depends on the sensor accuracy and on the reliability of the measure processing algorithm. The purpose of this article is to propose a low cost positioning system using internal sensors like odometers and optical fiber gyroscopes. Three simple localization algorithms based on different sensor data processing procedures are presented. Two of them operate in a deterministic framework, the third operates in a stochastic framework where the uncertainty is induced by sensing and unmodeled robot dynamics. The performance of the proposed localization algorithms are tested through a wide set of laboratory experiments and compared in terms of localization accuracy and computational cost. © 2005 Wiley Periodicals, Inc.

Sonar and Video Data Fusion for Robot Localization and Environment Feature Estimation

Abstract: In this paper the localization and environment feature estimation problems are formulated in a stochastic setting, and an Extended Kalman Filtering (EKF) approach is proposed for the integration of odometric, video camera and sonar measures. The environment is supposed to be only partially known, and a probabilistic method for sensory data fusion aimed at increasing the environment knowledge is considered.

Experimental validation of FastSLAM algorithm characterized by a linear feature based map

Abstract: In this paper the Simultaneous Localization and Mapping Problem in an unknown indoor environment is addressed. A probabilistic approach based on FastSLAM algorithm and line feature map is described. Experimental results performed with a real mobile base in an indoor environment are introduced and discussed.