A probabilistic model for appearance-based robot localization

in Image & Vision Computing (2001), 19(6), 381-391

In this paper we present a method for an appearance-based modeling of the environment of a mobile robot. We describe the task (localization of the robot) in a probabilistic framework. Linear image ... [more ▼]

In this paper we present a method for an appearance-based modeling of the environment of a mobile robot. We describe the task (localization of the robot) in a probabilistic framework. Linear image features are extracted using a Principal Component Analysis. The appearance model is represented as a probability density function of the image feature vector given the location of the robot. We estimate this density model from the data with a kernel estimation method. We show how the parameters of the model influence the localization performance. We also study how many features and which features are needed for good localization. (C) 2001 Elsevier Science B.V. All rights reserved. [less ▲]

Supervised linear feature extraction for mobile robot localization

in Proc. IEEE Int. Conf. on Robotics and Automation (2000)

We are seeking linear projections of supervised high-dimensional robot observations and an appropriate environment model that optimize the robot localization task. We show that an appropriate risk ... [more ▼]

We are seeking linear projections of supervised high-dimensional robot observations and an appropriate environment model that optimize the robot localization task. We show that an appropriate risk function to minimize is the conditional entropy of the robot positions given the projected observations. We propose a method of iterative optimization through a probabilistic model based on kernel smoothing. To obtain good starting optimization solutions we use canonical correlation analysis. We apply our method on a real experiment involving a mobile robot equipped with an omnidirectional camera in an office setup. [less ▲]

Mixture density estimation based on Maximum Likelihood and test statistics

in Neural Processing Letters (1999), 9(1), 63-76

A kurtosis-based dynamic approach to Gaussian mixture modeling

in IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans (1999), 29(4), 393-399

We address the problem of probability density function estimation using a Gaussian mixture model updated with the expectation-maximization (EM) algorithm. To deal with the case of an unknown number of ... [more ▼]

We address the problem of probability density function estimation using a Gaussian mixture model updated with the expectation-maximization (EM) algorithm. To deal with the case of an unknown number of mixing kernels, we define a new measure for Gaussian mixtures, called total kurtosis, which is based on the weighted sample kurtoses of the kernels. This measure provides an indication of how well the Gaussian mixture fits the data. Then we propose a new dynamic algorithm for Gaussian mixture density estimation which monitors the total kurtosis at each step of the Ehl algorithm in order to decide dynamically on the correct number of kernels and possibly escape from local maxima. We show the potential of our technique in approximating unknown densities through a series of examples with several density estimation problems. [less ▲]

Appearance-Based Robot Localization

in Proc. IJCAI'99, 16th Int. Joint Conf. on Artificial Intelligence, ROB-2 Workshop (1999)

A Sensory Uncertainty Field Model for Unknown and Non-stationary Mobile Robot Environments

in Proc. IEEE Int. Conf. on Robotics and Automation (1998)

An Experiment for Truly Parallel Logic Programming

in Journal of Intelligent & Robotic Systems (1996), 16(2), 169-184