Munguia Rodrigo, Urzua Sarquis, Grau Antoni
Department of Computer Science, CUCEI, University of Guadalajara, Guadalajara, México.
Automatic Control Dept, Technical University of Catalonia, 08034 Barcelona, Spain.
PLoS One. 2016 Dec 29;11(12):e0167197. doi: 10.1371/journal.pone.0167197. eCollection 2016.
In recent years, many researchers have addressed the issue of making Unmanned Aerial Vehicles (UAVs) more and more autonomous. In this context, the state estimation of the vehicle position is a fundamental necessity for any application involving autonomy. However, the problem of position estimation could not be solved in some scenarios, even when a GPS signal is available, for instance, an application requiring performing precision manoeuvres in a complex environment. Therefore, some additional sensory information should be integrated into the system in order to improve accuracy and robustness. In this work, a novel vision-based simultaneous localization and mapping (SLAM) method with application to unmanned aerial vehicles is proposed. One of the contributions of this work is to design and develop a novel technique for estimating features depth which is based on a stochastic technique of triangulation. In the proposed method the camera is mounted over a servo-controlled gimbal that counteracts the changes in attitude of the quadcopter. Due to the above assumption, the overall problem is simplified and it is focused on the position estimation of the aerial vehicle. Also, the tracking process of visual features is made easier due to the stabilized video. Another contribution of this work is to demonstrate that the integration of very noisy GPS measurements into the system for an initial short period of time is enough to initialize the metric scale. The performance of this proposed method is validated by means of experiments with real data carried out in unstructured outdoor environments. A comparative study shows that, when compared with related methods, the proposed approach performs better in terms of accuracy and computational time.
近年来，许多研究人员致力于使无人机（UAV）越来越自主化。在此背景下，对于任何涉及自主性的应用而言，飞行器位置的状态估计都是一项基本需求。然而，即使在有GPS信号的情况下，位置估计问题在某些场景中仍无法解决，例如在复杂环境中需要执行精确机动的应用。因此，应将一些额外的传感信息集成到系统中，以提高准确性和鲁棒性。在这项工作中，提出了一种适用于无人机的基于视觉的新型同步定位与地图构建（SLAM）方法。这项工作的贡献之一是设计并开发了一种基于随机三角测量技术的估计特征深度的新技术。在所提出的方法中，相机安装在一个伺服控制的万向节上，该万向节可抵消四轴飞行器姿态的变化。基于上述假设，整体问题得以简化，并且专注于飞行器的位置估计。此外，由于视频稳定，视觉特征的跟踪过程也变得更加容易。这项工作的另一个贡献是证明，在系统初始的短时间内将噪声很大的GPS测量值集成到系统中足以初始化度量尺度。通过在非结构化户外环境中进行的真实数据实验验证了所提出方法的性能。一项比较研究表明，与相关方法相比，所提出的方法在准确性和计算时间方面表现更好。
