Localization under consistent assumptions over dynamics

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

Abstrakti

Accurate maps are a prerequisite for virtually all mobile robot tasks. Most state-of-the-art maps assume a static world; therefore, dynamic objects are filtered out of the measurements. However, this division ignores movable but non-moving-i.e., semistatic-objects, which are usually recorded in the map and treated as static objects, violating the static world assumption and causing errors in the localization. This paper presents a method for consistently modeling moving and movable objects to match the map and measurements. This reduces the error resulting from inconsistent categorization and treatment of non-static measurements. A semantic segmentation network is used to categorize the measurements into static and semi-static classes, and a background subtraction filter is used to remove dynamic measurements. Finally, we show that consistent assumptions over dynamics improve localization accuracy when compared against a state-of-the-art baseline solution using real-world data from the Oxford Radar RobotCar data set.

AlkuperäiskieliEnglanti
Otsikko2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2024
KustantajaIEEE
ISBN (elektroninen)979-8-3503-6803-1
ISBN (painettu)979-8-3503-6804-8
DOI - pysyväislinkit
TilaJulkaistu - 4 syysk. 2024
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE International Conference on Multisensor Fusion and Integration - Pilsen, Tshekki
Kesto: 4 syysk. 20246 syysk. 2024

Julkaisusarja

NimiProceedings of IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems
ISSN (elektroninen)2767-9357

Conference

ConferenceIEEE International Conference on Multisensor Fusion and Integration
Maa/AlueTshekki
KaupunkiPilsen
Ajanjakso04/09/202406/09/2024

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