— This paper describes a cooperative method for relative localization of mobile robot teams; that is, it describes a method whereby every robot in the team can estimate the pose ...
Andrew Howard, Maja J. Mataric, Gaurav S. Sukhatme
We study maximum a posteriori probability model order selection for linear regression models, assuming Gaussian distributed noise and coefficient vectors. For the same data model,...
Abstract— This paper considers two approaches to the problem of vision and self-localization on a mobile robot. In the first approach, the perceptual processing is primarily bot...
This paper examines high dimensional regression with noise-contaminated input and output data. Goals of such learning problems include optimal prediction with noiseless query poin...
To operate successfully in indoor environments, mobile robots must be able to localize themselves. Most current localization algorithms lack flexibility, autonomy, and often optim...