Edge-based features from omnidirectional images for robot localization

in Proc. IEEE Int. Conf. on Robotics and Automation (2001)

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 ▲]

An information-theoretic localization criterion for robot map building

in Proc. ACAI'99, Int. Conf. on Machine Learning and Applications (1999)

Mixture Conditional Density Estimation with the EM Algorithm

in Proc. 9th Int. Conf. on Artificial Neural Networks (1999)

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 ▲]

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

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

Global Path Planning for Autonomous Qualitative Navigation

in Proc. 8th IEEE Int. Conf. on Tools with AI (1996)