تقرير
Object Instance Retrieval in Assistive Robotics: Leveraging Fine-Tuned SimSiam with Multi-View Images Based on 3D Semantic Map
| العنوان: | Object Instance Retrieval in Assistive Robotics: Leveraging Fine-Tuned SimSiam with Multi-View Images Based on 3D Semantic Map |
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| المؤلفون: | Sakaguchi, Taichi, Taniguchi, Akira, Hagiwara, Yoshinobu, Hafi, Lotfi El, Hasegawa, Shoichi, Taniguchi, Tadahiro |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Robotics |
| الوصف: | Robots that assist in daily life are required to locate specific instances of objects that match the user's desired object in the environment. This task is known as Instance-Specific Image Goal Navigation (InstanceImageNav), which requires a model capable of distinguishing between different instances within the same class. One significant challenge in robotics is that when a robot observes the same object from various 3D viewpoints, its appearance may differ greatly, making it difficult to recognize and locate the object accurately. In this study, we introduce a method, SimView, that leverages multi-view images based on a 3D semantic map of the environment and self-supervised learning by SimSiam to train an instance identification model on-site. The effectiveness of our approach is validated using a photorealistic simulator, Habitat Matterport 3D, created by scanning real home environments. Our results demonstrate a 1.7-fold improvement in task accuracy compared to CLIP, which is pre-trained multimodal contrastive learning for object search. This improvement highlights the benefits of our proposed fine-tuning method in enhancing the performance of assistive robots in InstanceImageNav tasks. The project website is https://emergentsystemlabstudent.github.io/MultiViewRetrieve/. Comment: See website at https://emergentsystemlabstudent.github.io/MultiViewRetrieve/. Submitted to IROS2024 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2404.09647 |
| رقم الأكسشن: | edsarx.2404.09647 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |