In order to improve usability and safety, modern unmanned aerial vehicles (UAVs) are equipped with sensors to monitor the environment, such as laser-scanners and cameras. One important aspect in this monitoring process is to detect obstacles in the flight path in order to avoid collisions. Since a large number of consumer UAVs suffer from tight weight and power constraints, our work focuses on obstacle avoidance based on a lightweight stereo camera setup. We use disparity maps, which are computed from the camera images, to locate obstacles and to automatically steer the UAV around them. For disparity map computation we optimize the well-known semi-global matching (SGM) approach for the deployment on an embedded FPGA. The disparity maps are then converted into simpler representations, the so called U-/V-Maps, which are used for obstacle detection. Obstacle avoidance is based on a reactive approach which finds the shortest path around the obstacles as soon as they have a critical distance to the UAV. One of the fundamental goals of our work was the reduction of development costs by closing the gap between application development and hardware optimization. Hence, we aimed at using high-level synthesis (HLS) for porting our algorithms, which are written in C/C++, to the embedded FPGA. We evaluated our implementation of the disparity estimation on the KITTI Stereo 2015 benchmark. The integrity of the overall realtime reactive obstacle avoidance algorithm has been evaluated by using Hardware-in-the-Loop testing in conjunction with two flight simulators.
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机译：为了提高可用性和安全性，现代无人飞行器（UAV）配备了用于监视环境的传感器，例如激光扫描仪和照相机。该监视过程中的一个重要方面是检测飞行路径中的障碍物，以避免碰撞。由于许多消费类无人机都受到严格的重量和功率限制，因此我们的工作重点是基于轻巧的立体摄像机设置来避开障碍物。我们使用从摄像机图像计算出的视差图来定位障碍物，并自动引导无人机绕过它们。对于视差图计算，我们针对嵌入式FPGA上的部署优化了众所周知的半全局匹配（SGM）方法。然后将视差图转换成更简单的表示形式，即所谓的U- / V-Map，用于障碍物检测。避障基于一种反应性方法，该方法可在障碍物与无人机之间有关键距离时立即找到障碍物附近的最短路径。我们工作的基本目标之一是通过缩小应用程序开发和硬件优化之间的差距来降低开发成本。因此，我们旨在使用高级综合（HLS）将用C / C ++编写的算法移植到嵌入式FPGA。我们根据KITTI Stereo 2015基准评估了视差估计的实施情况。整体实时反应性避障算法的完整性已通过结合两个飞行模拟器的硬件在环测试进行了评估。
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