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.

翻译：农业任务自动化的加速推进要求田间机器人具备高精度且鲁棒的定位系统。同时定位与地图构建（SLAM）方法在探索性轨迹上不可避免地产生累积漂移，主要依赖场所重访与回环检测将全局定位误差控制在一定范围内。由于农业场景中不同视角的局部视觉外观高度相似，且易受天气影响，回环检测技术面临显著挑战。实践中一个可行的替代方案是将全局传感器定位系统与机器人其他传感器联合使用。本文提出并实现了融合全球导航卫星系统（GNSS）、立体视觉与惯性测量的定位方案。具体而言，我们以紧耦合方式将GNSS测量值集成到立体-惯性ORB-SLAM3流程中。在Rosario数据集（由自主机器人大豆田采集的序列）及自采数据集上进行了全面评估，其中包含主流方法评估中较少涉及的常规GNSS测量数据。我们表征了GNSS-立体-惯性SLAM在该应用场景中的性能表现，与视觉-惯性基线及松耦合GNSS-立体-惯性基线相比，位姿误差降低了10%至30%。除上述分析外，我们还将算法实现代码以开源形式发布。