Observability analysis and active control for airborne SLAM
Observability analysis and active control for airborne SLAM
复制标题
DOI：
10.1109/taes.2008.4517003
复制
发表时间：
2008-01-01
影响因子：
4.4
通讯作者：
Sukkarieh, Salah
中科院分区：
文献类型：
--
作者：
Bryson, Mitch;Sukkarieh, Salah
无人机(UAV)的任务是探索未知环境并绘制其发现的特征地图，但必须在不使用基于基础设施的定位系统(如GPS)或任何先验地形数据的情况下完成这一任务。无人机使用一种称为同步定位和映射(SLAM)的统计估计技术进行导航，该技术允许同时估计无人机的位置以及它所看到的特征的位置。SLAM为车辆定位提供了一种独特的方法，潜在的应用包括行星探测，或者当GPS被拒绝时(例如，在故意的GPS干扰下，或者在GPS信号无法到达的应用中)，但更重要的是，可以用于增强现有的系统，以提高对导航故障的稳健性。SLAM工作的一个关键要求是必须重新观察特征，这有两个效果：第一，改进了特征的位置估计；第二，由于将平台与特征联系在一起的统计相关性，改善了平台的位置估计。因此，我们的无人机有两个选择：它是应该探索更多未知的地形来发现新的特征，还是应该重新访问已知的特征以提高定位质量。本文介绍了SLAM算法，并对该算法的两个重要性质进行了评估，为无人机路径规划模块的开发提供了帮助。第一种是使用概率的“熵”度量作为地图和车辆位置的确定性的基于信息的度量，并被用作规划无人机轨迹和确定观察地图中的特征的顺序的效用函数。第二种是SLAM的可观性分析，它给出了依赖于车辆机动的不可观测状态。该分析规定了无人机在观察特征时所需的机动类型，以保持对地图和车辆位置的准确统计估计。利用这两个特性，我们展示了一个在线路径规划器，它在探索未知地形的同时智能地规划车辆的轨迹，以最大化地图和车辆位置的质量。利用无人机六自由度模拟器，给出了在线航迹规划算法的仿真结果。结果表明，车辆定位误差受到约束，地图的特征数和地图尺寸在夜间稳步增长。
An unmanned aerial vehicle (UAV) is tasked to explore an unknown environment and to map the features it finds, but must do so without the use of infrastructure-based localisation systems such as GPS, or any a priori terrain data. The UAV navigates using a statistical estimation technique known as simultaneous localisation and mapping (SLAM) which allows for the simultaneous estimation of the location of the UAV as well as the location of the features it sees. SLAM offers a unique approach to vehicle localisation with potential applications including planetary exploration, or when GPS is denied (for example under intentional GPS jamming, or applications where GPS signals cannot be reached), but more importantly can be used to augment already existing systems to improve robustness to navigation failure.One key requirement for SLAM to work is that it must reobserve features, and this has two effects: firstly, the improvement of the location estimate of the feature; and secondly, the improvement of the location estimate of the platform because of the statistical correlations that link the platform to the feature. So our UAV has two options; should it explore more unknown terrain to find new features, or should it revisit known features to improve localisation quality. These options are instantiated into the online path planner for the UAV.We present the SLAM algorithm and evaluate two important properties about the algorithm which assist in developing a path planning module for the UAV. The first of these is the use of the probabilistic measure of "entropy" as an information-based measure of the certainty in the map and vehicle locations, and is used as a utility function for planning the UAVs trajectory and determining the order in which features in the map are observed. The second is an observability analysis of SLAM which presents the unobservable states which are dependent on vehicle maneuvers. The analysis dictates the type of manoeuvres required by the UAV while observing features in order to maintain accurate statistical estimates of the map and vehicle location. This has the effect of reducing the action space that the path planner needs to search over.Using these two properties, we demonstrate an online path planner that intelligently plans the vehicle's trajectory while exploring unknown terrain in order to maximise the quality of both the map and vehicle location. Results of the online path planning algorithm are presented using a 6-DoF simulator of our UAV. The results show that the vehicle localisation errors are constrained and that the number of features and the size of the map steadily grows during the night.