Altmetric

Optimized Self-Localization for SLAM in Dynamic Scenes using Probability Hypothesis Density Filters

File Description SizeFormat 
08116669.pdfPublished version1.8 MBAdobe PDFView/Open
Title: Optimized Self-Localization for SLAM in Dynamic Scenes using Probability Hypothesis Density Filters
Authors: Evers, C
Naylor, PA
Item Type: Journal Article
Abstract: In many applications, sensors that map the positions of objects in unknown environments are installed on dynamic platforms. As measurements are relative to the observer's sensors, scene mapping requires accurate knowledge of the observer state. However, in practice, observer reports are subject to positioning errors. Simultaneous Localization and Mapping (SLAM) addresses the joint estimation problem of observer localization and scene mapping. State-of-the-art approaches typically use visual or optical sensors and therefore rely on static beacons in the environment to anchor the observer estimate. However, many applications involving sensors that are not conventionally used for SLAM are affected by highly dynamic scenes, such that the static world assumption is invalid. This paper proposes a novel approach for dynamic scenes, called GEneralized Motion (GEM)-SLAM. Based on Probability Hypothesis Density (PHD) filters, the proposed approach probabilistically anchors the observer state by fusing observer information inferred from the scene with reports of the observer motion. This paper derives the general, theoretical framework for GEM-SLAM and shows that it generalizes existing PHD-based SLAM algorithms. Simulations for a model-specific realization using range-bearing sensors and multiple moving objects highlight that GEM-SLAM achieves significant improvements over three benchmark algorithms.
Issue Date: 21-Nov-2017
Date of Acceptance: 1-Nov-2017
URI: http://hdl.handle.net/10044/1/53603
DOI: https://dx.doi.org/10.1109/TSP.2017.2775590
ISSN: 1053-587X
Publisher: Institute of Electrical and Electronics Engineers
Start Page: 863
End Page: 878
Journal / Book Title: IEEE Transactions on Signal Processing
Volume: 66
Issue: 4
Copyright Statement: This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/.
Sponsor/Funder: Commission of the European Communities
Engineering & Physical Science Research Council (E
Funder's Grant Number: 609465
EP/P001017/1
Keywords: MD Multidisciplinary
Networking & Telecommunications
Publication Status: Published
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Creative Commons