用GPU加速视觉SLAM中的回环检测，速度提升超3倍。
FastLoop: Parallel Loop Closing with GPU-Acceleration in Visual SLAM
- 在GPU上并行处理回环检测任务，优化计算流程
- 在桌面与嵌入式平台分别提速1.4~3.0倍，精度不变
- 适配ORB-SLAM3，适合实时机器人定位场景
视觉SLAM系统通过视觉追踪与全局回环闭合来维持一致的地图和精确的定位。回环闭合过程计算量大，需在整个地图中搜索匹配项。本文提出FastLoop，一个基于GPU加速的回环闭合模块，以缓解计算复杂性。我们识别出视觉SLAM回环闭合管道中的关键性能瓶颈，并通过GPU上的任务级与数据级并行优化加以解决。具体包括采用任务级和数据级并行，并集成GPU加速的姿态图优化。该实现基于ORB-SLAM3，使用CUDA进行GPU编程。实验结果表明，FastLoop在EuRoC数据集上桌面与嵌入式平台分别获得平均1.4倍和1.3倍加速，在TUM-VI数据集上分别达到3.0倍和2.4倍加速，同时保持原系统的精度。
原文摘要 · Abstract (English)
Visual SLAM systems combine visual tracking with global loop closure to maintain a consistent map and accurate localization. Loop closure is a computationally expensive process as we need to search across the whole map for matches. This paper presents FastLoop, a GPU-accelerated loop closing module to alleviate this computational complexity. We identify key performance bottlenecks in the loop closing pipeline of visual SLAM and address them through parallel optimizations on the GPU. Specifically, we use task-level and data-level parallelism and integrate a GPU-accelerated pose graph optimization. Our implementation is built on top of ORB-SLAM3 and leverages CUDA for GPU programming. Experimental results show that FastLoop achieves an average speedup of 1.4x and 1.3x on the EuRoC dataset and 3.0x and 2.4x on the TUM-VI dataset for the loop closing module on desktop and embedded platforms, respectively, while maintaining the accuracy of the original system.
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