论文标题
使用集成深度传感器（Rappids）的矩形金字塔分配：多层导航的快速计划器
Rectangular Pyramid Partitioning using Integrated Depth Sensors (RAPPIDS): A Fast Planner for Multicopter Navigation
论文作者
论文摘要
我们提出了Rappids：一种新颖的碰撞检查和计划算法，可用于多杆，能够快速找到来自板载摄像头的单个深度图像的本地无碰撞轨迹。这项工作的主要贡献是一种新的基于金字塔的空间分配方法，可在候选轨迹和环境之间进行快速碰撞检测。通过利用我们的碰撞检查方法的效率，我们展示了如何在计算约束的硬件上以高速率运行本地规划算法，从而评估了数千个毫秒的候选轨迹。将算法的性能与模拟中的现有碰撞检查方法进行了比较，这表明我们的方法能够评估每秒更多的轨迹。提出了实验结果，显示了四肢旋转器，通过在30 Hz的odroid-XU4上运行算法，迅速导航了以前看不见的混乱环境。
We present RAPPIDS: a novel collision checking and planning algorithm for multicopters that is capable of quickly finding local collision-free trajectories given a single depth image from an onboard camera. The primary contribution of this work is a new pyramid-based spatial partitioning method that enables rapid collision detection between candidate trajectories and the environment. By leveraging the efficiency of our collision checking method, we shown how a local planning algorithm can be run at high rates on computationally constrained hardware, evaluating thousands of candidate trajectories in milliseconds. The performance of the algorithm is compared to existing collision checking methods in simulation, showing our method to be capable of evaluating orders of magnitude more trajectories per second. Experimental results are presented showing a quadcopter quickly navigating a previously unseen cluttered environment by running the algorithm on an ODROID-XU4 at 30 Hz.
