This paper presents a novel collision-free navigation system for the unmanned aerial vehicle based on point clouds that outperform compared to baseline methods, enabling high-speed flights in cluttered environments, such as forests or many indoor industrial plants. The algorithm takes the point cloud information from physical sensors (e.g. lidar, depth camera) and then converts it to an occupied map using Voxblox, which is then used by a rapid-exploring random tree to generate finite path candidates. A modified Covariant Hamiltonian Optimization for Motion Planning objective function is used to select the best candidate and update it. Finally, the best candidate trajectory is generated and sent to a Model Predictive Control controller. The proposed navigation strategy is evaluated in four different simulation environments; the results show that the proposed method has a better success rate and a shorter goal-reaching distance than the baseline method.

本文提出了一种基于点云的无人驾驶飞机新型无碰撞导航系统，其性能优于基线方法，可在森林或许多室内工厂等混乱环境中实现高速飞行。该算法从物理传感器（例如，激光雷达，深度相机）获取点云信息，然后使用Voxblox将其转换为占用地图，然后由快速探索的随机树使用它来生成有限路径候选。修改后的运动计划协方差汉密尔顿优化目标函数用于选择最佳候选者并对其进行更新。最后，生成最佳候选轨迹并将其发送到模型预测控制控制器。在四种不同的仿真环境中评估了所提出的导航策略。