Robotic mapping is attractive in many scientific applications that involve environmental surveys. This paper presents a system for localization and mapping of sparsely distributed surface features such as precariously balanced rocks (PBRs), whose geometric fragility parameters provide valuable information on earthquake processes and landscape development. With this geomorphologic problem as the test domain, we carry out a lawn-mower search pattern from a high elevation using an Unpiloted Aerial Vehicle (UAV) equipped with a flight controller, GPS module, stereo camera, and onboard computer. Once a potential PBR target is detected by a deep neural network in real time, we track its bounding box in the image coordinates by applying a Kalman filter that fuses the deep learning detection with Kanade-Lucas-Tomasi (KLT) tracking. The target is localized in world coordinates using depth filtering where a set of 3D points are filtered by object bounding boxes from different camera perspectives. The 3D points also provide a strong prior on target shape, which is used for UAV path planning to closely map the target using RGBD SLAM. After target mapping, the UAS resumes the lawn-mower search pattern to locate and map the next target.

中文翻译：

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使用无人驾驶飞机系统定位和测绘稀疏地质特征

机器人测绘在许多涉及环境调查的科学应用中很有吸引力。本文提出了一种用于定位和制图稀疏分布的表面特征的系统，例如不稳定的平衡岩石 (PBR)，其几何脆性参数提供了有关地震过程和景观发展的宝贵信息。以这个地貌问题为测试域，我们使用配备有飞行控制器、GPS 模块、立体摄像头和机载计算机的无人驾驶飞行器 (UAV) 从高处执行割草机搜索模式。一旦深度神经网络实时检测到潜在的 PBR 目标，我们就会通过应用卡尔曼滤波器在图像坐标中跟踪其边界框，该滤波器将深度学习检测与 Kanade-Lucas-Tomasi (KLT) 跟踪相融合。使用深度过滤将目标定位在世界坐标中，其中一组 3D 点由来自不同相机视角的对象边界框过滤。3D 点还提供了对目标形状的强先验，可用于 UAV 路径规划，以使用 RGBD SLAM 密切映射目标。目标映射后，UAS 恢复割草机搜索模式以定位和映射下一个目标。