Conventional wheeled robots are unable to traverse scientifically interesting, but dangerous, cave environments. Multi-limbed climbing robot designs, such as ReachBot, are able to grasp irregular surface features and execute climbing motions to overcome obstacles, given suitable grasp locations. To support grasp site identification, we present a method for detecting rock cracks and edges, the SKeleton Intersection Loss (SKIL). SKIL is a loss designed for thin object segmentation that leverages the skeleton of the label. A dataset of rock face images was collected, manually annotated, and augmented with generated data. A new group of metrics, LineAcc, has been proposed for thin object segmentation such that the impact of the object width on the score is minimized. In addition, the metric is less sensitive to translation which can often lead to a score of zero when computing classical metrics such as Dice on thin objects. Our fine-tuned models outperform previous methods on similar thin object segmentation tasks such as blood vessel segmentation and show promise for integration onto a robotic system.

翻译：传统轮式机器人无法穿越具有科学价值但危险的洞穴环境。多肢体攀爬机器人设计（如ReachBot）能够抓取不规则表面特征并执行攀爬动作以克服障碍——前提是需要合适的抓取点。为支持抓取点识别，我们提出了一种检测岩石裂缝与边缘的方法：骨架交叉熵损失（SKIL）。SKIL是一种专为薄目标分割设计的损失函数，利用标签的骨架信息。我们收集了岩石表面图像数据集，进行了人工标注，并通过生成数据进行了扩充。针对薄目标分割提出了新的评价指标组LineAcc，使得目标宽度对评分的影响最小化。此外，该指标对平移不敏感——计算Dice等经典指标时，薄目标因平移常导致得分为零。我们的微调模型在血管分割等类似薄目标分割任务上优于先前方法，并展现出集成至机器人系统的潜力。