Robotic mapping is attractive in many science 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 (stability) parameters provide valuable information on earthquake processes. With geomorphology as the test domain, we carry out a lawnmower search pattern using an Unpiloted Aerial Vehicle (UAV) equipped with a GPS module, stereo camera, and onboard computers. Once a target is detected by a deep neural network, we track its bounding box in the image coordinates by applying a Kalman filter that fuses the deep learning detection with 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 accurately map the target using RGBD SLAM. After target mapping, the UAS resumes the lawnmower search pattern to locate the next target. Our end goal is a real-time mapping methodology for sparsely distributed surface features on earth or on extraterrestrial surfaces.
翻译:机器人绘图在许多涉及环境调查的科学应用中具有吸引力。 本文展示了一种系统,用于定位和绘制分布稀疏的表面特征,如不稳定平衡岩石(PBRs),其几何脆弱性(稳定性)参数为地震过程提供了宝贵的信息。以地貌学为测试领域,我们使用配备全球定位系统模块、立体相机和机载计算机的无人驾驶航空飞行器(UAV)进行草坪搜索模式。一旦一个目标被深神经网络探测到,我们就会通过应用KLT跟踪的Kalman过滤器在图像坐标上跟踪它的捆绑框。这个目标将利用深度过滤器将深度的学习探测与KLT连接起来。这个目标位于世界范围内,其中一组3D点由不同摄像角度的物体捆绑框过滤。 3D点还提供了在目标形状上的强烈的前方位图, 用于UAVAV路径规划使用RGBD SLAM来准确绘制目标。 目标绘图后, UAS将恢复草模搜索模式, 以定位下一个目标。 我们最终的目标是对地表或地表层上分散分布的地表地貌进行实时测绘。