Abstract This paper deals with terrain mapping and position estimation using multiple robots. Here we will discuss work where a larger group of robots can mutually estimate one another's position (in 2D or 3D) and uncertainty using a sample-based (particle filter) model of uncertainty. Our prior work has dealt with a pair of robots that estimate one another's position using visual tracking and coordinated motion and we extend these results and consider a richer set of sensing and motion options. In particular, we focus on issues related to confidence estimation for groups of more than two robots.