4.7 Article

Localizing Ground Penetrating RADAR: A Step Toward Robust Autonomous Ground Vehicle Localization

期刊

JOURNAL OF FIELD ROBOTICS
卷 33, 期 1, 页码 82-102

出版社

WILEY
DOI: 10.1002/rob.21605

关键词

-

类别

向作者/读者索取更多资源

Autonomous ground vehicles navigating on road networks require robust and accurate localization over long-term operation and in a wide range of adverse weather and environmental conditions. GPS/INS (inertial navigation system) solutions, which are insufficient alone to maintain a vehicle within a lane, can fail because of significant radio frequency noise or jamming, tall buildings, trees, and other blockage or multipath scenarios. LIDAR and camera map-based vehicle localization can fail when optical features become obscured, such as with snow or dust, or with changes to gravel or dirt road surfaces. Localizing ground penetrating radar (LGPR) is a new mode of a priori map-based vehicle localization designed to complement existing approaches with a low sensitivity to failure modes of LIDAR, camera, and GPS/INS sensors due to its low-frequency RF energy, which couples deep into the ground. Most subsurface features detected are inherently stable over time. Significant research, discussed herein, remains to prove general utility. We have developed a novel low-profile ultra-low power LGPR system and demonstrated real-time operation underneath a passenger vehicle. A correlation maximizing optimization technique was developed to allow real-time localization at 126Hz. Here we present the detailed design and results from highway testing, which uses a simple heuristic for fusing LGPR estimates with a GPS/INS system. Cross-track localization accuracies of 4.3cm RMS relative to a truth RTK GPS/INS unit at speeds up to 100km/h (60mph) are demonstrated. These results, if generalizable, introduce a widely scalable real-time localization method with cross-track accuracy as good as or better than current localization methods.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据