3 research outputs found
A Convex Approach to Consensus on SO(n)
This paper introduces several new algorithms for consensus over the special
orthogonal group. By relying on a convex relaxation of the space of rotation
matrices, consensus over rotation elements is reduced to solving a convex
problem with a unique global solution. The consensus protocol is then
implemented as a distributed optimization using (i) dual decomposition, and
(ii) both semi and fully distributed variants of the alternating direction
method of multipliers technique -- all with strong convergence guarantees. The
convex relaxation is shown to be exact at all iterations of the dual
decomposition based method, and exact once consensus is reached in the case of
the alternating direction method of multipliers. Further, analytic and/or
efficient solutions are provided for each iteration of these distributed
computation schemes, allowing consensus to be reached without any online
optimization. Examples in satellite attitude alignment with up to 100 agents,
an estimation problem from computer vision, and a rotation averaging problem on
validate the approach.Comment: Accepted to 52nd Annual Allerton Conference on Communication,
Control, and Computin
Convex Model Predictive Control for Vehicular Systems
In this work, we present a method to perform Model Predictive Control (MPC)
over systems whose state is an element of for . This is done
without charts or any local linearization, and instead is performed by
operating over the orbitope of rotation matrices. This results in a novel MPC
scheme without the drawbacks associated with conventional linearization
techniques. Instead, second order cone- or semidefinite-constraints on state
variables are the only requirement beyond those of a QP-scheme typical for MPC
of linear systems. Of particular emphasis is the application to aeronautical
and vehicular systems, wherein the method removes many of the transcendental
trigonometric terms associated with these systems' state space equations.
Furthermore, the method is shown to be compatible with many existing variants
of MPC, including obstacle avoidance via Mixed Integer Linear Programming
(MILP)
Collaborative Perception From Data Association To Localization
During the last decade, visual sensors have become ubiquitous. One or more cameras
can be found in devices ranging from smartphones to unmanned aerial vehicles and
autonomous cars. During the same time, we have witnessed the emergence of large
scale networks ranging from sensor networks to robotic swarms.
Assume multiple visual sensors perceive the same scene from different viewpoints. In
order to achieve consistent perception, the problem of correspondences between ob-
served features must be first solved. Then, it is often necessary to perform distributed
localization, i.e. to estimate the pose of each agent with respect to a global reference
frame. Having everything set in the same coordinate system and everything having
the same meaning for all agents, operation of the agents and interpretation of the
jointly observed scene become possible.
The questions we address in this thesis are the following: first, can a group of visual
sensors agree on what they see, in a decentralized fashion? This is the problem of
collaborative data association. Then, based on what they see, can the visual sensors
agree on where they are, in a decentralized fashion as well? This is the problem of
cooperative localization.
The contributions of this work are five-fold. We are the first to address the problem
of consistent multiway matching in a decentralized setting. Secondly, we propose
an efficient decentralized dynamical systems approach for computing any number of
smallest eigenvalues and the associated eigenvectors of a weighted graph with global
convergence guarantees with direct applications in group synchronization problems,
e.g. permutations or rotations synchronization. Thirdly, we propose a state-of-the
art framework for decentralized collaborative localization for mobile agents under
the presence of unknown cross-correlations by solving a minimax optimization prob-
lem to account for the missing information. Fourthly, we are the first to present an
approach to the 3-D rotation localization of a camera sensor network from relative
bearing measurements. Lastly, we focus on the case of a group of three visual sensors.
We propose a novel Riemannian geometric representation of the trifocal tensor which
relates projections of points and lines in three overlapping views. The aforemen-
tioned representation enables the use of the state-of-the-art optimization methods on
Riemannian manifolds and the use of robust averaging techniques for estimating the
trifocal tensor