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首页> 外文期刊>IEEE transactions on industrial informatics >Autonomous State Estimation and Mapping in Unknown Environments With Onboard Stereo Camera for Micro Aerial Vehicles
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Autonomous State Estimation and Mapping in Unknown Environments With Onboard Stereo Camera for Micro Aerial Vehicles

机译：用于微型航空车辆车载立体声摄像机的自主状态估计和映射

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Industrial micro aerial vehicles (MAVs) with robotic manipulators have numerous applications in search and rescue tasks that reduce risks to human beings. However, such tasks distinctly require MAVs to have the capability of real-time autonomous navigation only with onboard sensors, especially in GPS-denied applications. This article introduces a new approach to onboard vision-based autonomous state estimation and mapping for MAVs' navigation in unknown environments. The algorithms run on board and do not need an external positioning system to assist autonomous navigation. The state estimator is developed to provide MAV's current pose on the basis of the extended Kalman filter by using image patch features. Inverse depth convergence monitoring and local bundle adjustment are utilized to improve the accuracy. The mapping algorithm for navigation is developed according to a real-time stereo matching method for three-dimensional perception. Finally, we have performed several experiments to demonstrate the effectiveness of the proposed approach.
机译：工业微空气车辆（MAVS）与机器人操纵器有许多在搜索和救援任务中的应用，这些任务可以减少人类的风险。但是，此类任务明显要求MAVS仅具有实时自主导航的能力，仅使用船坞传感器，尤其是GPS拒绝应用程序。本文介绍了一种新的船上视觉的自主状态估算和Mavs在未知环境中导航的映射。算法在船上运行，不需要外部定位系统来协助自动导航。通过使用图像修补程序功能，开发了状态估计器以提供MAV的当前姿势。逆深度收敛监测和局部捆绑调整用于提高精度。导航映射算法​​是根据三维感知的实时立体声匹配方法开发的。最后，我们已经表演了几个实验来证明所提出的方法的有效性。

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