تقرير
Artifacts Mapping: Multi-Modal Semantic Mapping for Object Detection and 3D Localization
| العنوان: | Artifacts Mapping: Multi-Modal Semantic Mapping for Object Detection and 3D Localization |
|---|---|
| المؤلفون: | Rollo, Federico, Raiola, Gennaro, Zunino, Andrea, Tsagarakis, Nikolaos, Ajoudani, Arash |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Robotics, Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | Geometric navigation is nowadays a well-established field of robotics and the research focus is shifting towards higher-level scene understanding, such as Semantic Mapping. When a robot needs to interact with its environment, it must be able to comprehend the contextual information of its surroundings. This work focuses on classifying and localising objects within a map, which is under construction (SLAM) or already built. To further explore this direction, we propose a framework that can autonomously detect and localize predefined objects in a known environment using a multi-modal sensor fusion approach (combining RGB and depth data from an RGB-D camera and a lidar). The framework consists of three key elements: understanding the environment through RGB data, estimating depth through multi-modal sensor fusion, and managing artifacts (i.e., filtering and stabilizing measurements). The experiments show that the proposed framework can accurately detect 98% of the objects in the real sample environment, without post-processing, while 85% and 80% of the objects were mapped using the single RGBD camera or RGB + lidar setup respectively. The comparison with single-sensor (camera or lidar) experiments is performed to show that sensor fusion allows the robot to accurately detect near and far obstacles, which would have been noisy or imprecise in a purely visual or laser-based approach. Comment: Accepted to the 11th European Conference on Mobile Robots (ECMR) 2023 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2307.01121 |
| رقم الأكسشن: | edsarx.2307.01121 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |