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Optimized self-localization for SLAM in dynamic scenes using probability hypothesis density filters

Optimized self-localization for SLAM in dynamic scenes using probability hypothesis density filters
Optimized self-localization for SLAM in dynamic scenes using probability hypothesis density filters
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 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 Simultaneous Localization and Mapping (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 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 Probability Hypothesis Density (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.
1053-587X
863-878
Evers, Christine
93090c84-e984-4cc3-9363-fbf3f3639c4b
Naylor, Patrick
8c20a1a0-4507-4a0f-8324-f3075354dc52
Evers, Christine
93090c84-e984-4cc3-9363-fbf3f3639c4b
Naylor, Patrick
8c20a1a0-4507-4a0f-8324-f3075354dc52

Evers, Christine and Naylor, Patrick (2018) Optimized self-localization for SLAM in dynamic scenes using probability hypothesis density filters. IEEE Transactions on Signal Processing, 66 (4), 863-878. (doi:10.1109/TSP.2017.2775590).

Record type: 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 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 Simultaneous Localization and Mapping (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 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 Probability Hypothesis Density (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.

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Accepted/In Press date: 1 April 2016
e-pub ahead of print date: 21 November 2017
Published date: 15 February 2018

Identifiers

Local EPrints ID: 438598
URI: http://eprints.soton.ac.uk/id/eprint/438598
ISSN: 1053-587X
PURE UUID: 274b20eb-30c1-4c82-ba06-44d050d3ee75
ORCID for Christine Evers: ORCID iD orcid.org/0000-0003-0757-5504

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Date deposited: 18 Mar 2020 17:30
Last modified: 19 Mar 2020 01:45

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