Distributed Multi-Robot Cooperative Localization Using Bayesian Fusion on the Special Euclidean Group
Embargo until
2015-05-01
Date
2014-05-07
Authors
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Journal ISSN
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Publisher
Johns Hopkins University
Abstract
This thesis presents a new distributed cooperative localization technique using a second
order sensor fusion method developed for the Special Euclidean group. Uncertainties
in the robot pose, sensor measurements and landmark positions (neighboring robots in
this case) are modeled as Gaussian distributions in exponential coordinates. This proves
to be a better t for posterior distributions resulting from the motion of nonholonomic
kinematic systems with stochastic noise (compared to standard Gaussians in Cartesian
coordinates). We provide a recursive closed-form solution to the multi-sensor fusion
problem that can be used to incorporate a large number of sensor measurements into
the localization routine and can be implemented in real time. The technique can be
used for nonlinear sensor models without the need for further simpli cations given that
the required relative pose and orientation information can be provided, and it is scalable
in that the computational complexity does not increase with the size of the robot team
and increases linearly with the number of measurements taken from nearby robots. The
proposed approach is validated with simulation rst conceptually in Matlab then more
realistically in the robotics simulator ROS/Gazebo. It is also compared with one of the
current state of the art methods (distributed EKF) and shows promising results.
Description
Keywords
multi-robot, distributed, cooperative localization, Lie group, exponential coordinate