Robotic mapping is useful in scientific applications that involve surveying unstructured environments. This paper presents a target-oriented mapping system for sparsely distributed geologic surface features, such as precariously balanced rocks (PBRs), whose geometric fragility parameters can provide valuable information on earthquake shaking history and landscape development for a region. With this geomorphology problem as the test domain, we demonstrate a pipeline for detecting, localizing, and precisely mapping fragile geologic features distributed on a landscape. To do so, we first carry out a lawn-mower search pattern in the survey region from a high elevation using an Unpiloted Aerial Vehicle (UAV). Once a potential PBR target is detected by a deep neural network, we track the bounding box in the image frames using a real-time tracking algorithm. The location and occupancy of the target in world coordinates are estimated using a sampling-based filtering algorithm, where a set of 3D points are re-sampled after weighting by the tracked bounding boxes from different camera perspectives. The converged 3D points provide a prior on 3D bounding shape of a target, which is used for UAV path planning to closely and completely map the target with Simultaneous Localization and Mapping (SLAM). After target mapping, the UAV resumes the lawn-mower search pattern to find the next target. We introduce techniques to make the target mapping robust to false positive and missing detection from the neural network. Our target-oriented mapping system has the advantages of reducing map storage and emphasizing complete visible surface features on specified targets.

中文翻译：

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易碎地质特征的自动机器人制图

机器人映射在涉及测量非结构化环境的科学应用中很有用。本文针对稀疏分布的地表特征，例如不稳定平衡的岩石（PBR），提供了一种面向目标的制图系统，该系统的几何脆弱性参数可以为地区的震颤历史和景观发展提供有价值的信息。以这个地貌问题作为测试领域，我们演示了用于检测，定位和精确映射分布在景观上的脆弱地质特征的管道。为此，我们首先使用无人驾驶飞机（UAV）在高海拔地区的调查区域内进行割草机搜索。一旦深度神经网络检测到潜在的PBR目标，我们使用实时跟踪算法跟踪图像帧中的边界框。使用基于采样的过滤算法来估计目标在世界坐标中的位置和占用率，该算法在跟踪的包围盒加权后从不同的摄像机角度对一组3D点进行重新采样。会聚的3D点提供了目标的先验3D边界形状，该形状用于UAV路径规划，以通过同步定位和映射（SLAM）紧密而完整地映射目标。在绘制目标之后，无人机会恢复割草机的搜索模式以找到下一个目标。我们介绍了使目标映射对神经网络的假阳性和漏检具有鲁棒性的技术。