LiDAR-Visual-Inertial Tightly-coupled Odometry with Adaptive Learnable Fusion Weights
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
LiDAR-Visual-Inertial Tightly-coupled Odometry with Adaptive Learnable Fusion Weights
Original language description
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.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
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
Number of pages
7
Pages from-to
5641-5647
Publisher name
IEEE
Place of publication
Piscataway
Event location
Abu Dhabi
Event date
Oct 14, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001411890000572