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.

翻译：语义地图是机器人导航与操作等任务的基础，在农业场景中还可实现产量预测与表型分析。本文提出一种高效可扩展的主动语义建图方法，适用于园艺环境，采用搭载RGB-D相机的移动机械臂平台。该方法利用概率语义地图检测语义目标、生成候选观测视点并计算对应信息增益。我们提出一种高效的光线投射策略及新颖的信息效用函数，该函数同时考虑语义信息与遮挡效应。相较于现有基线方法，所提方案将总运行时间降低8%。此外，在降低多类别熵与提升表面覆盖率方面，我们的信息度量指标优于其他度量方法，在存在分割噪声时表现尤为突出。真实环境实验验证了本方法的有效性，同时也揭示了深度传感器噪声与环境条件变化等挑战，这些问题有待进一步研究。