- Link:
- http://hdl.handle.net/10220/6535
- Collection:
-
- Subjects
- DRNTU::Engineering::Electrical and electronic engineering. DRNTU::Engineering::Electrical and electronic
engineering.
- Creators:
- Mullane, John. Vo, Ba-Ngu. Adams, Martin D.
- Contributors:
- School of Electrical and Electronic Engineering IEEE International Conference on Robotics and Automation.
- Format
- 8 p.
- Language
- en
- Rights
- © 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works
for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in otherworks must be obtained from the IEEE. This material is presented to ensure timely
dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to
the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. http://www.ieee.org/portal/site
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying
this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the
copyright holder.
- Type
- Conference Paper
- Description
- This paper proposes a tractable solution to
feature-based (FB) SLAM in the presence of data association
uncertainty and uncertainty in the number of features. By modeling
the feature map as a random finite set (RFS), a rigorous Bayesian
formulation of the FB-SLAM problem that accounts for uncertainty in
the number of features and data association is presented. As such,
the joint posterior distribution of the set-valued map and vehicle
trajectory is propagated forward in time as measurements arrive. A
first order solution, coined the PHD-SLAM filter, is derived, which
jointly propagates the posterior PHD or intensity function of the
map and the posterior distribution of the trajectory of the
vehicle. A Rao-Blackwellised implementation of the PHD-SLAM filter
is proposed based on the Gaussian mixture PHD filter for the map
and a particle filter for the vehicle trajectory. Simulated results
demonstrate the merits of the proposed approach, particularly in
situations of high clutter and data association
ambiguity.
- Rights
- © 2010 IEEE. Personal use of this material is permitted.
However, permission to reprint/republish this material for
advertising or promotional purposes or for creating new collective
works for resale or redistribution to servers or lists, or to reuse
any copyrighted component of this work in other works must be
obtained from the IEEE. This material is presented to ensure timely
dissemination of scholarly and technical work. Copyright and all
rights therein are retained by authors or by other copyright
holders. All persons copying this information are expected to
adhere to the terms and constraints invoked by each author's
copyright. In most cases, these works may not be reposted without
the explicit permission of the copyright holder.
http://www.ieee.org/portal/site This material is presented to
ensure timely dissemination of scholarly and technical work.
Copyright and all rights therein are retained by authors or by
other copyright holders. All persons copying this information are
expected to adhere to the terms and constraints invoked by each
author's copyright. In most cases, these works may not be reposted
without the explicit permission of the copyright
holder.
- Rights
- © 2010 IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other uses, in any
current or future media, including reprinting/republishing this
material for advertising or promotional purposes, creating new
collective works, for resale or redistribution to servers or lists,
or reuse of any copyrighted component of this work in other works.
The published version is available at: [DOI:
http://dx.doi.org/10.1109/ROBOT.2010.5509626].
- Visits:
- 1
- Access:
- Instructions in case access is denied
Site powered by:
