SLAM with SC-PHD filters: an underwater vehicle application
SLAM with SC-PHD filters: an underwater vehicle application
The random finite-set formulation for multiobject estimation provides a means of estimating the number of objects in cluttered environments with missed detections within a unified probabilistic framework. This methodology is now becoming the dominant mathematical framework within the sensor fusion community for developing multiple-target tracking algorithms. These techniques are also gaining traction in the field of feature-based simultaneous localization and mapping (SLAM) for mobile robotics. Here, we present one such instance of this approach with an underwater vehicle using a hierarchical multiobject estimation method for estimating both landmarks and vehicle position
38-45
Lee, Chee Sing
6bf0d264-fc24-490f-9561-1edc6e1ceb86
Nagappa, Sharad
372a311b-f1f2-46f1-99be-9677f58be4f5
Palomeras, N.
96ec084c-bd72-4279-a022-78cdd4d0d8ce
Clark, Daniel E.
537f80e8-cbe6-41eb-b1d4-31af1f0e6393
Salvi, Joaquim
988e1d19-ddab-4627-9303-2648feac9c87
7 May 2014
Lee, Chee Sing
6bf0d264-fc24-490f-9561-1edc6e1ceb86
Nagappa, Sharad
372a311b-f1f2-46f1-99be-9677f58be4f5
Palomeras, N.
96ec084c-bd72-4279-a022-78cdd4d0d8ce
Clark, Daniel E.
537f80e8-cbe6-41eb-b1d4-31af1f0e6393
Salvi, Joaquim
988e1d19-ddab-4627-9303-2648feac9c87
Lee, Chee Sing, Nagappa, Sharad, Palomeras, N., Clark, Daniel E. and Salvi, Joaquim
(2014)
SLAM with SC-PHD filters: an underwater vehicle application.
IEEE Robotics and Automation Magazine, 21 (2), .
(doi:10.1109/MRA.2014.2310132).
Abstract
The random finite-set formulation for multiobject estimation provides a means of estimating the number of objects in cluttered environments with missed detections within a unified probabilistic framework. This methodology is now becoming the dominant mathematical framework within the sensor fusion community for developing multiple-target tracking algorithms. These techniques are also gaining traction in the field of feature-based simultaneous localization and mapping (SLAM) for mobile robotics. Here, we present one such instance of this approach with an underwater vehicle using a hierarchical multiobject estimation method for estimating both landmarks and vehicle position
This record has no associated files available for download.
More information
Published date: 7 May 2014
Identifiers
Local EPrints ID: 474474
URI: http://eprints.soton.ac.uk/id/eprint/474474
PURE UUID: 1e0a24ec-c1c4-447f-94fd-20a3fce45a42
Catalogue record
Date deposited: 22 Feb 2023 21:08
Last modified: 16 Mar 2024 23:15
Export record
Altmetrics
Contributors
Author:
Chee Sing Lee
Author:
Sharad Nagappa
Author:
N. Palomeras
Author:
Daniel E. Clark
Author:
Joaquim Salvi
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics