LiDAR-Visual-Inertial Tightly-coupled Odometry with Adaptive Learnable Fusion Weights

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00382151" target="_blank" >RIV/68407700:21230/24:00382151 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/IROS58592.2024.10802180" target="_blank" >https://doi.org/10.1109/IROS58592.2024.10802180</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IROS58592.2024.10802180" target="_blank" >10.1109/IROS58592.2024.10802180</a>

Jazyk výsledku

angličtina

Název v původním jazyce

LiDAR-Visual-Inertial Tightly-coupled Odometry with Adaptive Learnable Fusion Weights

Popis výsledku v původním jazyce

In this paper, we address the sensitivity of the 3D LiDAR-based localization to environmental structural ambiguity. Although existing approaches employ additional sensors, such as cameras and inertial measurement units, to account for such ambiguities, multi-sensor localization is still an open problem. Limitations are from the need to tune fusion parameters to compensate for limited ambiguity detection manually. Therefore, we propose a feature-based localization method that learns the fusion parameters using ground truth and thus supports autonomous mobile robotic systems in new locations. The method combines planar surface LiDAR features with close and far camera features, and its further advantage is an online adjustment of the feature weights based on the measured environment ambiguity. The evaluation has been performed on the existing M2DGR dataset and custom dataset with geometrical ambiguities. The proposed method is competitive to or outperforms the existing LiDAR-based methods F-LOAM and LIO-SAM and the Visual-Inertial localization method VINS-Mono. Based on the reported results, the proposed method is a vital combination of LiDAR-based and visual features.

Název v anglickém jazyce

LiDAR-Visual-Inertial Tightly-coupled Odometry with Adaptive Learnable Fusion Weights

Popis výsledku anglicky

In this paper, we address the sensitivity of the 3D LiDAR-based localization to environmental structural ambiguity. Although existing approaches employ additional sensors, such as cameras and inertial measurement units, to account for such ambiguities, multi-sensor localization is still an open problem. Limitations are from the need to tune fusion parameters to compensate for limited ambiguity detection manually. Therefore, we propose a feature-based localization method that learns the fusion parameters using ground truth and thus supports autonomous mobile robotic systems in new locations. The method combines planar surface LiDAR features with close and far camera features, and its further advantage is an online adjustment of the feature weights based on the measured environment ambiguity. The evaluation has been performed on the existing M2DGR dataset and custom dataset with geometrical ambiguities. The proposed method is competitive to or outperforms the existing LiDAR-based methods F-LOAM and LIO-SAM and the Visual-Inertial localization method VINS-Mono. Based on the reported results, the proposed method is a vital combination of LiDAR-based and visual features.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)

ISBN

979-8-3503-7770-5

ISSN

2153-0858

e-ISSN

2153-0866

Počet stran výsledku

7

Strana od-do

5641-5647

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Abu Dhabi

Datum konání akce

14. 10. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001411890000572