Song Yu, Nuske Stephen, Scherer Sebastian
Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.
Sensors (Basel). 2016 Dec 22;17(1):11. doi: 10.3390/s17010011.
State estimation is the most critical capability for MAV (Micro-Aerial Vehicle) localization, autonomous obstacle avoidance, robust flight control and 3D environmental mapping. There are three main challenges for MAV state estimation: (1) it can deal with aggressive 6 DOF (Degree Of Freedom) motion; (2) it should be robust to intermittent GPS (Global Positioning System) (even GPS-denied) situations; (3) it should work well both for low- and high-altitude flight. In this paper, we present a state estimation technique by fusing long-range stereo visual odometry, GPS, barometric and IMU (Inertial Measurement Unit) measurements. The new estimation system has two main parts, a stochastic cloning EKF (Extended Kalman Filter) estimator that loosely fuses both absolute state measurements (GPS, barometer) and the relative state measurements (IMU, visual odometry), and is derived and discussed in detail. A long-range stereo visual odometry is proposed for high-altitude MAV odometry calculation by using both multi-view stereo triangulation and a multi-view stereo inverse depth filter. The odometry takes the EKF information (IMU integral) for robust camera pose tracking and image feature matching, and the stereo odometry output serves as the relative measurements for the update of the state estimation. Experimental results on a benchmark dataset and our real flight dataset show the effectiveness of the proposed state estimation system, especially for the aggressive, intermittent GPS and high-altitude MAV flight.
状态估计是微型飞行器（MAV）定位、自主避障、稳健飞行控制和三维环境测绘中最关键的能力。微型飞行器状态估计面临三个主要挑战：（1）它能够应对剧烈的六自由度运动；（2）它应能在GPS（全球定位系统）信号间歇性出现（甚至无GPS信号）的情况下保持稳健；（3）它应在低空和高空飞行中都能良好运行。在本文中，我们提出了一种通过融合长距离立体视觉里程计、GPS、气压计和惯性测量单元（IMU）测量值的状态估计技术。新的估计系统有两个主要部分，一个随机克隆扩展卡尔曼滤波器（EKF）估计器，它将绝对状态测量值（GPS、气压计）和相对状态测量值（IMU、视觉里程计）进行松散融合，并对其进行了详细推导和讨论。提出了一种长距离立体视觉里程计，用于通过多视图立体三角测量和多视图立体逆深度滤波器来计算高空微型飞行器的里程计。该里程计利用EKF信息（IMU积分）进行稳健的相机姿态跟踪和图像特征匹配，立体里程计输出用作状态估计更新的相对测量值。在一个基准数据集和我们的实际飞行数据集上的实验结果表明了所提出的状态估计系统的有效性，特别是对于剧烈运动、间歇性GPS和高空微型飞行器飞行情况。
