Localization under consistent assumptions over dynamics

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

Abstract

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.

Original languageEnglish
Title of host publication2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2024
PublisherIEEE
ISBN (Electronic)979-8-3503-6803-1
ISBN (Print)979-8-3503-6804-8
DOIs
Publication statusPublished - 4 Sept 2024
MoE publication typeA4 Conference publication
EventIEEE International Conference on Multisensor Fusion and Integration - Pilsen, Czech Republic
Duration: 4 Sept 20246 Sept 2024

Conference

ConferenceIEEE International Conference on Multisensor Fusion and Integration
Country/TerritoryCzech Republic
CityPilsen
Period04/09/202406/09/2024

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