Graduate Program in Electrical Engineering, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte-MG 31270-901, Brazil.
Department of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, WV 26506, USA.
Sensors (Basel). 2019 Sep 20;19(19):4061. doi: 10.3390/s19194061.
Autonomous navigation of unmanned vehicles in forests is a challenging task. In such environments, due to the canopies of the trees, information from Global Navigation Satellite Systems (GNSS) can be degraded or even unavailable. Also, because of the large number of obstacles, a previous detailed map of the environment is not practical. In this paper, we solve the complete navigation problem of an aerial robot in a sparse forest, where there is enough space for the flight and the GNSS signals can be sporadically detected. For localization, we propose a state estimator that merges information from GNSS, Attitude and Heading Reference Systems (AHRS), and odometry based on Light Detection and Ranging (LiDAR) sensors. In our LiDAR-based odometry solution, the trunks of the trees are used in a feature-based scan matching algorithm to estimate the relative movement of the vehicle. Our method employs a robust adaptive fusion algorithm based on the unscented Kalman filter. For motion control, we adopt a strategy that integrates a vector field, used to impose the main direction of the movement for the robot, with an optimal probabilistic planner, which is responsible for obstacle avoidance. Experiments with a quadrotor equipped with a planar LiDAR in an actual forest environment is used to illustrate the effectiveness of our approach.
无人车在森林中的自主导航是一项具有挑战性的任务。在这样的环境中，由于树冠的存在，全球导航卫星系统（GNSS）的信息可能会降级甚至不可用。此外，由于障碍物数量众多，之前详细的环境地图并不实用。在本文中，我们解决了在稀疏森林中飞行机器人的完整导航问题，其中有足够的飞行空间，并且可以偶尔检测到 GNSS 信号。对于定位，我们提出了一种状态估计器，该估计器融合了来自 GNSS、姿态和航向参考系统（AHRS）以及基于光检测和测距（LiDAR）传感器的里程计的信息。在我们基于 LiDAR 的里程计解决方案中，利用树木的树干在基于特征的扫描匹配算法中估计车辆的相对运动。我们的方法采用了一种基于无迹卡尔曼滤波器的鲁棒自适应融合算法。对于运动控制，我们采用了一种策略，该策略将用于为机器人施加主要运动方向的向量场与负责避免障碍物的最优概率规划器集成在一起。在实际森林环境中，配备平面 LiDAR 的四旋翼飞行器的实验用于说明我们方法的有效性。
