Abstract
The combination of aerial survey capabilities of Unmanned Aerial Vehicles with targeted intervention abilities of agricultural Unmanned Ground Vehicles can significantly improve the effectiveness of robotic systems applied to precision agriculture. In this context, building and updating a common map of the field is an essential but challenging task. The maps built using robots of different types show differences in size, resolution and scale, the associated geolocation data may be inaccurate and biased, while the repetitiveness of both visual appearance and geometric structures found within agricultural contexts render classical map merging techniques ineffective. In this paper we propose AgriColMap, a novel map registration pipeline that leverages a grid-based multimodal environment representation which includes a vegetation index map and a Digital Surface Model. We cast the data association problem between maps built from UAVs and UGVs as a multimodal, large displacement dense optical flow estimation. The dominant, coherent flows, selected using a voting scheme, are used as point-to-point correspondences to infer a preliminary non-rigid alignment between the maps. A final refinement is then performed, by exploiting only meaningful parts of the registered maps. We evaluate our system using real world data for 3 fields with different crop species. The results show that our method outperforms several state of the art map registration and matching techniques by a large margin, and has a higher tolerance to large initial misalignments. We release an implementation of the proposed approach along with the acquired datasets with this paper.
Abstract (translated)
无人机航测能力与农业无人机目标干预能力相结合，可以显著提高机器人系统在精准农业中的应用效果。在这种情况下，建立和更新一个共同的领域地图是一项重要但具有挑战性的任务。使用不同类型的机器人绘制的地图在尺寸、分辨率和比例尺上存在差异，相关的地理位置数据可能不准确并存在偏差，而在农业环境中发现的视觉外观和几何结构的重复性使经典的地图合并技术无效。本文提出了一种新的地图配准方法Agricolmap，它利用基于网格的多模式环境表示，包括植被指数图和数字地表模型。我们将无人机和无人值守地面车辆生成的地图之间的数据关联问题归结为多模态大位移密集光流估计。使用投票方案选择的主要一致流用作点对点对应，以推断地图之间的初步非刚性对齐。然后，通过只利用注册地图中有意义的部分，执行最终的细化。我们使用3个不同作物种类的田地的真实数据评估我们的系统。结果表明，该方法在很大程度上优于几种最先进的地图配准和匹配技术，对较大的初始偏差具有较高的容忍度。我们在本文中发布了该方法的实现以及所获得的数据集。
URL
https://arxiv.org/abs/1810.00457
