张宁 Data Flow ORB-SLAM for Real-Time Performance on Embedded GPU Boards
数据流ORB-SLAM可在嵌入式GPU板上实现实时性能
链接：https://pan.baidu.com/s/1MoMDI-FIunkNWHbvDBSfXQ
提取码：8wjo
Stefano Aldegheri1, Nicola Bombieri1, Domenico D. Bloisi2, and Alessandro Farinelli1
The use of embedded boards on robots, including unmanned aerial and ground vehicles, is increasing thanks to the availability of GPU equipped low-cost embedded boards in the market. Porting algorithms originally designed for desktop CPUs on those boards is not straightforward due to hardware limitations. In this paper, we present how we modified and customized the open source SLAM algorithm ORB-SLAM2 to run in real-time on the NVIDIA Jetson TX2. We adopted a data flow paradigm to process the images, obtaining an efficient CPU/GPU load distribution that results in a processing speed of about 30 frames per second. Quantitative experimental results on four different sequences of the KITTI datasets demonstrate the effectiveness of the proposed approach. The source code of our data flow ORB-SLAM2 algorithm is publicly available on GitHub.
得益于市场上配备GPU的低成本嵌入式板的可用性，包括无人驾驶飞机和地面车辆在内的机器人在嵌入式板上的使用正在增加。由于硬件限制，最初为这些板上的台式机CPU设计的移植算法并不简单。在本文中，我们介绍了如何修改和自定义开源SLAM算法ORB-SLAM2，以便在NVIDIA Jetson TX2上实时运行。我们采用数据流范式来处理图像，从而获得有效的CPU / GPU负载分配，从而导致每秒约30帧的处理速度。在KITTI数据集的四个不同序列上的定量实验结果证明了该方法的有效性。 我们的数据流ORB-SLAM2算法的源代码可在GitHub上公开获得。