地面编码：基于学习因子图的探地雷达定位模型
Ground Encoding: Learned Factor Graph-based Models for Localizing Ground Penetrating Radar
摘要：
我们使用探地雷达（GPR）传感器解决机器人定位的问题。目前使用GPR传感器进行定位的方法需要系统环境的先验地图以及在操作期间访问近似全球定位（GPS）。在本文中，我们提出了一种新的，实时的基于GPRS的定位系统，未知和GPS拒绝的环境。我们将本地化问题建模为因子图上的推理。我们的方法结合了一维单通道GPR测量，形成二维图像子地图。为了在图中使用这些GPR图像，我们需要能够将嘈杂的高维图像测量映射到状态空间中的传感器模型。这些是具有挑战性的先验获得，因为图像生成具有复杂的依赖于地下成分和雷达物理，这本身与传感器和地下电磁特性的变化而变化。我们的主要思想是，而不是学习相对传感器模型直接从GPR数据映射非连续GPR图像对相对机器人运动。这些模型被合并为因子图内的因子，其中相对运动预测校正位置估计中的累积漂移。我们使用定制设计的实验装置在多个位置收集的数据集上演示了我们的方法。我们只使用GPR和里程计测量不同的轨迹在三个不同的GPS拒绝的环境中显示可靠的，实时的定位。有关我们的补充视频，请参阅www.example.com。
英文摘要：
We address the problem of robot localization using ground penetrating radar (GPR) sensors. Current approaches for localization with GPR sensors require a priori maps of the system's environment as well as access to approximate global positioning (GPS) during operation. In this paper, we propose a novel, real-time GPR-based localization system for unknown and GPS-denied environments. We model the localization problem as an inference over a factor graph. Our approach combines 1D single-channel GPR measurements to form 2D image submaps. To use these GPR images in the graph, we need sensor models that can map noisy, high-dimensional image measurements into the state space. These are challenging to obtain a priori since image generation has a complex dependency on subsurface composition and radar physics, which itself varies with sensors and variations in subsurface electromagnetic properties. Our key idea is to instead learn relative sensor models directly from GPR data that map non-sequential GPR image pairs to relative robot motion. These models are incorporated as factors within the factor graph with relative motion predictions correcting for accumulated drift in the position estimates. We demonstrate our approach over datasets collected across multiple locations using a custom designed experimental rig. We show reliable, real-time localization using only GPR and odometry measurements for varying trajectories in three distinct GPS-denied environments. For our supplementary video, see https://youtu.be/HXXgdTJzqyw.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
