面向耕作农业的GNSS-双目-惯性SLAM
GNSS-stereo-inertial SLAM for arable farming
- CIFASIS, French Argentine International Center for Information and Systems Sciences (CONICET-UNR)（法阿国际信息与系统科学中心（国家科学技术研究委员会-罗萨里奥国立大学））
- University of Zaragoza（萨拉戈萨大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对农田场景SLAM回环检测难、定位易漂移的问题，提出将GNSS以紧耦合方式融入双目惯性ORB-SLAM3的定位方法，在大豆田数据集上实现10%-30%的位姿误差降低，并开源代码。
AI中文摘要：
农业任务自动化进程的加快，对田间机器人的高精度、高鲁棒性定位系统提出了需求。同时定位与地图构建（SLAM）方法在探索性轨迹上不可避免地会产生累积漂移，主要依赖位置重访与回环检测来将全局定位误差控制在有限范围内。回环检测技术在农田场景中极具挑战性，因为不同视角的局部视觉外观高度相似，且易受天气影响发生变化。实际应用中一种合适的替代方案是将全局传感器定位系统与机器人的其他传感器结合使用。本文提出并实现了全球导航卫星系统（GNSS）、双目图像与惯性测量数据的融合定位方法。具体而言，我们以紧耦合的方式将GNSS测量值集成到双目惯性ORB-SLAM3框架中。我们在Rosario数据集（由自主机器人在大豆田采集的序列）和自研内部数据集上对实现方案进行了全面评估，我们的数据集包含常规GNSS的测量数据，这类数据很少被纳入现有前沿方法的评估中。我们表征了GNSS-双目-惯性SLAM在该应用场景下的性能，结果显示，与视觉惯性基线和松耦合GNSS-双目-惯性基线相比，位姿误差降低了10%至30%。除上述分析外，我们还将实现代码以开源形式发布。
英文摘要：
The accelerating pace in the automation of agricultural tasks demands highly accurate and robust localization systems for field robots. Simultaneous Localization and Mapping (SLAM) methods inevitably accumulate drift on exploratory trajectories and primarily rely on place revisiting and loop closing to keep a bounded global localization error. Loop closure techniques are significantly challenging in agricultural fields, as the local visual appearance of different views is very similar and might change easily due to weather effects. A suitable alternative in practice is to employ global sensor positioning systems jointly with the rest of the robot sensors. In this paper we propose and implement the fusion of global navigation satellite system (GNSS), stereo views, and inertial measurements for localization purposes. Specifically, we incorporate, in a tightly coupled manner, GNSS measurements into the stereo-inertial ORB-SLAM3 pipeline. We thoroughly evaluate our implementation in the sequences of the Rosario data set, recorded by an autonomous robot in soybean fields, and our own in-house data. Our data includes measurements from a conventional GNSS, rarely included in evaluations of state-of-the-art approaches. We characterize the performance of GNSS-stereo-inertial SLAM in this application case, reporting pose error reductions between 10% and 30% compared to visual-inertial and loosely coupled GNSS-stereo-inertial baselines. In addition to such analysis, we also release the code of our implementation as open source.
