用移动机械臂在园艺环境主动构建语义地图，提升信息获取效率。
Active Semantic Mapping with Mobile Manipulator in Horticultural Environments
- 基于概率语义图生成候选视角，优化观测选择策略。
- 相比基线减少8%运行时间，多类熵降低且覆盖更全面。
- 适合农业机器人导航与作物表型分析场景使用。
语义地图是机器人导航与操作任务的基础，在农业环境中还可用于产量预测和表型分析。本文提出一种高效可扩展的园艺环境主动语义映射方法，采用配备RGB-D相机的移动机械臂。该方法利用概率语义图检测语义目标，生成候选视角并计算信息增益。提出一种高效的射线投射策略和新颖的信息效用函数，同时考虑语义与遮挡因素。相比以往基线方法，总运行时间减少8%。此外，所提信息度量在降低多类别熵和提升表面覆盖率方面优于其他指标，尤其在存在分割噪声时表现更优。真实世界实验验证了方法的有效性，但也揭示深度传感器噪声与环境条件变化带来的挑战，需进一步研究。
原文摘要 · Abstract (English)
Semantic maps are fundamental for robotics tasks such as navigation and manipulation. They also enable yield prediction and phenotyping in agricultural settings. In this paper, we introduce an efficient and scalable approach for active semantic mapping in horticultural environments, employing a mobile robot manipulator equipped with an RGB-D camera. Our method leverages probabilistic semantic maps to detect semantic targets, generate candidate viewpoints, and compute corresponding information gain. We present an efficient ray-casting strategy and a novel information utility function that accounts for both semantics and occlusions. The proposed approach reduces total runtime by 8% compared to previous baselines. Furthermore, our information metric surpasses other metrics in reducing multi-class entropy and improving surface coverage, particularly in the presence of segmentation noise. Real-world experiments validate our method's effectiveness but also reveal challenges such as depth sensor noise and varying environmental conditions, requiring further research.
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