Autonomous navigation of micro aerial vehicles using high-rate and low-cost sensors
Autonomous navigation of micro aerial vehicles using high-rate and low-cost sensors
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DOI：
10.1007/s10514-017-9690-5
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发表时间：
2017-12
影响因子：
3.5
通讯作者：
Angel Santamaria-Navarro;Giuseppe Loianno;J. Solà;Vijay R. Kumar;J. Andrade-Cetto
中科院分区：
文献类型：
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作者：
Angel Santamaria-Navarro;Giuseppe Loianno;J. Solà;Vijay R. Kumar;J. Andrade-Cetto
视觉和惯性传感器相结合的状态估计由于其数据的轻量化和互补性，近年来在机器人领域，特别是在航空机器人领域得到了广泛的反响。然而，大多数基于视觉惯性感知的状态估计系统都受到严格的处理器要求的影响，这在许多情况下使它们不切实际。本文提出了一种简单、低成本、高速率的微型飞行器自主飞行状态估计方法，计算量小。所提出的状态估计器融合了来自惯性测量单元、光流智能相机和飞行时间距离传感器的观测结果。智能相机提供高达200hz的光流测量，避免了所有图像处理要求对主处理器产生的计算瓶颈。据我们所知，这是将这些智能相机的使用范围从悬停运动扩展到里程计估计的第一个例子，在几分钟的飞行时间内产生可用的估计。为了验证和捍卫最简单的算法解决方案，我们研究了扩展和错误状态两种卡尔曼滤波器的性能，以及在早期视觉惯性里程计文献中捍卫的大量算法修改，表明它们对滤波器性能的影响是最小的。为了关闭控制回路，在特殊欧氏群pse(3)中工作的非线性控制器能够根据估计的飞行器状态驱动三维空间中的四旋翼平台，保证三维位置和航向的渐近稳定性。所有的估计和控制任务都是在有限的计算单元上实时解决的。通过仿真和实验结果验证了所提出的方法，其中包括与运动捕捉系统提供的真实数据的比较。为了社区的利益，我们将源代码公开。
The combination of visual and inertial sensors for state estimation has recently found wide echo in the robotics community, especially in the aerial robotics field, due to the lightweight and complementary characteristics of the sensors data. However, most state estimation systems based on visual-inertial sensing suffer from severe processor requirements, which in many cases make them impractical. In this paper, we propose a simple, low-cost and high rate method for state estimation enabling autonomous flight of micro aerial vehicles, which presents a low computational burden. The proposed state estimator fuses observations from an inertial measurement unit, an optical flow smart camera and a time-of-flight range sensor. The smart camera provides optical flow measurements up to a rate of 200 Hz, avoiding the computational bottleneck to the main processor produced by all image processing requirements. To the best of our knowledge, this is the first example of extending the use of these smart cameras from hovering-like motions to odometry estimation, producing estimates that are usable during flight times of several minutes. In order to validate and defend the simplest algorithmic solution, we investigate the performances of two Kalman filters, in the extended and error-state flavors, alongside with a large number of algorithm modifications defended in earlier literature on visual-inertial odometry, showing that their impact on filter performance is minimal. To close the control loop, a non-linear controller operating in the special Euclidean groupSE(3) is able to drive, based on the estimated vehicle’s state, a quadrotor platform in 3D space guaranteeing the asymptotic stability of 3D position and heading. All the estimation and control tasks are solved on board and in real time on a limited computational unit. The proposed approach is validated through simulations and experimental results, which include comparisons with ground-truth data provided by a motion capture system. For the benefit of the community, we make the source code public.