基于嵌入式GPU的pyramid LK光流法高速计算方法研究

在嵌入式计算平台上实现双向约束LK金字塔高精度光流的实时计算，是该算法能否用于自动驾驶等场景的重要影响因素。为了实现该目的，提出了基于网格划分的特征提取方法及新的双向约束方法；然后设计了动态窗口的金字塔模型，解决了光流计算过程中的负载不均衡问题；最后通过降低计算位宽，使得整体性能获得进一步提升。实验结果表明：在Jetson TX2上，针对真实场景所用的720P视频，所提出方法的性能比OpenCV的GPU版本提升了4.1倍，达到30 fps以上；将采用该方法的SLAM系统成功应用于车载场景并在真实环境中测试，使得系统的性能达到了28 fps。新方法有效地提升了位姿和点云的精度，较好地满足了车载场景的实时处理需求。

High-speed computing of pyramid LK optical flow based on embedded GPU

Real-time calculation of high-precision forward-backward pyramid LK optical flow on embedded computing platform has become the decisive factor of whether the algorithm could be used in realistic scenarios such as autonomous driving. This paper presented a feature extraction method based on mesh division and a new forward-backward constraint method. To address the load unbalance in the optical flow calculation process, this paper proposed a dynamic window-based pyramid model. It also reduced the calculation bit width to further improve the overall performance. Using the 720P videos in realistic scenarios, this method yields significant performance gained over the GPU optimized version of OpenCV by 4.1 times on the embedded GPU platform TX2 and achieved 30 fps performance. It helped the SLAM system increase the frame rate to 28 fps in real car scene. This method improves the accuracy of the pose and point cloud in realistic scenarios, meeting the real-time application requirements of vehicle scenarios.