detalle del documento
IDENTIFICACIÓN
oai:arXiv.org:2409.11692
Tema
Computer Science - Computer Vision...Autor
Jin, Yanlin Ju, Rui-Yang Liu, Haojun Zhong, YuzhongCategoría
Computer Science
Año
2024
fecha de cotización
26/2/2025
Palabras clave
selective adaptation online generalizability visual odometryResumen
Deep visual odometry, despite extensive research, still faces limitations in accuracy and generalizability that prevent its broader application.
To address these challenges, we propose an Oriented FAST and Rotated BRIEF (ORB)-guided visual odometry with selective online adaptation named ORB-SfMLearner.
We present a novel use of ORB features for learning-based ego-motion estimation, leading to more robust and accurate results.
We also introduce the cross-attention mechanism to enhance the explainability of PoseNet and have revealed that driving direction of the vehicle can be explained through the attention weights.
To improve generalizability, our selective online adaptation allows the network to rapidly and selectively adjust to the optimal parameters across different domains.
Experimental results on KITTI and vKITTI datasets show that our method outperforms previous state-of-the-art deep visual odometry methods in terms of ego-motion accuracy and generalizability.
;Comment: Accepted to ICRA 2025; Project page: https://www.neiljin.site/projects/orbsfm/
Jin, Yanlin,Ju, Rui-Yang,Liu, Haojun,Zhong, Yuzhong, 2024, ORB-SfMLearner: ORB-Guided Self-supervised Visual Odometry with Selective Online Adaptation
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