在线光束平差法的高速计算方法研究

光束平差法（bundle adjustment，BA）是同步定位和地图构建（simultaneous localization and mapping，SLAM）后端优化的关键技术。在线使用光束平差时能否满足实时性要求，是将其应用于自动驾驶车端等实时系统的关键因素。首先分析特定场景中SLAM数据特点，提出滑动窗口机制降低计算规模；分析局部BA计算中稀疏矩阵性质提升算法的可并行性；最后基于嵌入式GPU对算法进行并行加速。将其应用于车载SLAM系统并在真实场景下测试，实验结果表明，在AGX Xavier嵌入式GPU上，针对720P道路场景，该方法比同平台CPU上处理性能平均提升4.8倍，可以处理15 fps的相机位姿地图数据，满足了30 fps的视频处理需求，达到了车载系统的实时性要求。

Research on high speed calculation method of online bundle adjustment

The bundle adjustment(BA) is a key technology for the back-end optimization of simultaneous positioning and map construction(SLAM). Using BA online could meet real-time requirements is a key factor in applying it to real-time systems such as autonomous driving vehicles. This paper firstly analyzed the characteristics of SLAM data in real scene and proposed a method using sliding window to reduce the calculation scale. Then it featured the parallelism of the algorithm by analyzing the properties of sparse matrices in local BA. Finally, it implemented and accelerated the algorithm based on the embedded GPU. Applied and tested into a vehicle-mounted SLAM system in a real scene, the experimental results show that it has 4.8 times higher processing performance than the CPU on the same platform on average on the AGX Xavier with embedded GPU for 720P images from road scenes. It can also handle position, pose and map data at 15 fps, which meets the real-time requirements of the vehicle-mounted system.