提出自适应光照补偿模型，提升机器人在暗光下抓取衣物的准确性。
GraspALL: Adaptive Structural Compensation from Illumination Variation for Robotic Garment Grasping in Any Low-Light Conditions
- 通过量化光照变化，动态调节可见光与非可见光特征融合方式。
- 在多种低光条件下，抓取准确率提升32%-44%。
- 适合需要全天候作业的服务机器人场景。
在动态光照变化下实现精准衣物抓取对服务机器人全天候运行至关重要。然而，低光环境下衣物结构特征严重退化，导致抓取鲁棒性显著下降。现有方法通常利用非可见光模态的光照不变特性增强RGB特征，但忽略了不同光照条件下对非可见光特征依赖程度的变化，可能引入错位的非可见光线索，削弱多模态信息融合的适应能力。为此，本文提出GraspALL，一种光照-结构交互式补偿模型。其核心在于将连续光照变化编码为定量参考，根据光照强度动态引导RGB与非可见光模态的特征融合，生成光照一致的抓取表征。在自建衣物抓取数据集上的实验表明，GraspALL在多种光照条件下相比基线方法抓取准确率提升32%-44%。
原文摘要 · Abstract (English)
Achieving accurate garment grasping under dynamically changing illumination is crucial for all-day operation of service robots.However, the reduced illumination in low-light scenes severely degrades garment structural features, leading to a significant drop in grasping robustness.Existing methods typically enhance RGB features by exploiting the illumination-invariant properties of non-RGB modalities, yet they overlook the varying dependence on non-RGB features under varying lighting conditions, which can introduce misaligned non-RGB cues and thereby weaken the model's adaptability to illumination changes when utilizing multimodal information.To address this problem, we propose GraspALL, an illumination-structure interactive compensation model.The innovation of GraspALL lies in encoding continuous illumination changes into quantitative references to guide adaptive feature fusion between RGB and non-RGB modalities according to varying lighting intensities, thereby generating illumination-consistent grasping representations.Experiments on the self-built garment grasping dataset demonstrate that GraspALL improves grasping accuracy by 32-44% over baselines under diverse illumination conditions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。