Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) |
| ISBN (elektronisch) | 979-8-3503-6803-1 |
| Publikationsstatus | Veröffentlicht - 2024 |
Publikationsreihe
| Name | IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems |
|---|---|
| ISSN (Print) | 2835-947X |
| ISSN (elektronisch) | 2767-9357 |
Abstract
Registration of point clouds is a fundamental task in robotic SLAM pipelines. Typically this task is performed only on point clouds of the same sensor or at least the same sensing modality. However, robots designed for challenging environments are often equipped with redundant sensors for the same task where some sensors are more accurate and others are more robust against disturbing environmental conditions. Being able to register the data across the modalities is an important step to more fault-tolerant localization and mapping. We therefore propose a learning framework, which describes the points in the point cloud invariant of their modality. This description is then used in a transformer-like model to find point matches for the registration process. We demonstrate our results using a scanning lidar and radar sensor on our own and publicly available datasets.
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2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). 2024. (IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Learning of Multimodal Point Descriptors in Radar and LIDAR Point Clouds
AU - Rotter, Jan M.
AU - Cohrs, Simon
AU - Blume, Holger
AU - Wagner, Bernardo
N1 - Publisher Copyright: © 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Registration of point clouds is a fundamental task in robotic SLAM pipelines. Typically this task is performed only on point clouds of the same sensor or at least the same sensing modality. However, robots designed for challenging environments are often equipped with redundant sensors for the same task where some sensors are more accurate and others are more robust against disturbing environmental conditions. Being able to register the data across the modalities is an important step to more fault-tolerant localization and mapping. We therefore propose a learning framework, which describes the points in the point cloud invariant of their modality. This description is then used in a transformer-like model to find point matches for the registration process. We demonstrate our results using a scanning lidar and radar sensor on our own and publicly available datasets.
AB - Registration of point clouds is a fundamental task in robotic SLAM pipelines. Typically this task is performed only on point clouds of the same sensor or at least the same sensing modality. However, robots designed for challenging environments are often equipped with redundant sensors for the same task where some sensors are more accurate and others are more robust against disturbing environmental conditions. Being able to register the data across the modalities is an important step to more fault-tolerant localization and mapping. We therefore propose a learning framework, which describes the points in the point cloud invariant of their modality. This description is then used in a transformer-like model to find point matches for the registration process. We demonstrate our results using a scanning lidar and radar sensor on our own and publicly available datasets.
UR - http://www.scopus.com/inward/record.url?scp=85207854260&partnerID=8YFLogxK
U2 - 10.1109/mfi62651.2024.10705777
DO - 10.1109/mfi62651.2024.10705777
M3 - Conference contribution
SN - 979-8-3503-6804-8
T3 - IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems
BT - 2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)
ER -