Controlling hand exoskeletons to assist individuals with grasping tasks poses
a challenge due to the difficulty in understanding user intentions. We propose
that most daily grasping tasks during activities of daily living (ADL) can be
deduced by analyzing object geometries (simple and complex) from 3D point
clouds. The study introduces PointGrasp, a real-time system designed for
identifying household scenes semantically, aiming to support and enhance
assistance during ADL for tailored end-to-end grasping tasks. The system
comprises an RGB-D camera with an inertial measurement unit and a
microprocessor integrated into a tendon-driven soft robotic glove. The RGB-D
camera processes 3D scenes at a rate exceeding 30 frames per second. The
proposed pipeline demonstrates an average RMSE of 0.8 $\pm$ 0.39 cm for simple
and 0.11 $\pm$ 0.06 cm for complex geometries. Within each mode, it identifies
and pinpoints reachable objects. This system shows promise in end-to-end
vision-driven robotic-assisted rehabilitation manual tasks.

通过分析三维点云中的对象几何形状（简单和复杂），提出了一种基于 PointGrasp 的实时系统，用于识别日常生活中的家居场景，并支持和增强日常活动中的定制化端到端抓取任务，实现可达对象的定位和识别。该系统在简单和复杂几何形状的情况下，平均 RMSE 为 0.8±0.39 cm 和 0.11±0.06 cm，展示了在视觉驱动的机器人辅助康复手动任务中的潜力。