  The 3D depth estimation and relative pose estimation problem within a
decentralized architecture is a challenging problem that arises in missions
that require coordination among multiple vision-controlled robots. The depth
estimation problem aims at recovering the 3D information of the environment.
The relative localization problem consists of estimating the relative pose
between two robots, by sensing each other's pose or sharing information about
the perceived environment. Most solutions for these problems use a set of
discrete data without taking into account the chronological order of the
events. This paper builds on recent results on continuous estimation to propose
a framework that estimates the depth and relative pose between two
non-holonomic vehicles. The basic idea consists in estimating the depth of the
points by explicitly considering the dynamics of the camera mounted on a ground
robot, and feeding the estimates of 3D points observed by both cameras in a
filter that computes the relative pose between the robots. We evaluate the
convergence for a set of simulated scenarios and show experimental results
validating the proposed framework.
