用GPU加速视觉SLAM中的局部建图，提升实时性且不损失精度。
TurboMap: GPU-Accelerated Local Mapping for Visual SLAM
- 将关键点匹配与地图点融合过程并行化到GPU，减少计算延迟。
- 在EuRoC和TUM-VI数据集上分别实现1.3倍和1.6倍的加速效果。
- 适合追求高实时性的机器人导航与自动驾驶系统开发者。
在实时视觉SLAM系统中，局部建图必须满足严格的延迟要求，否则会降低地图质量并增加跟踪失败风险。GPU并行化为降低延迟提供了可能，但因共享状态同步和大型地图数据结构传输开销，实现并行化极具挑战。本文提出TurboMap，一种兼顾GPU并行与CPU优化的局部建图后端，全面解决上述问题。通过重构地图点创建流程，实现在GPU上并行进行关键点匹配搜索；重新设计并行化地图点融合；在CPU端优化冗余关键帧剔除；集成快速的基于GPU的局部束调整求解器。为最小化数据传输与同步开销，引入持久驻留于GPU的关键帧存储机制。在EuRoC和TUM-VI数据集上的实验表明，平均局部建图速度分别提升1.3倍和1.6倍，同时保持原有精度。
原文摘要 · Abstract (English)
In real-time Visual SLAM systems, local mapping must operate under strict latency constraints, as delays degrade map quality and increase the risk of tracking failure. GPU parallelization offers a promising way to reduce latency. However, parallelizing local mapping is challenging due to synchronized shared-state updates and the overhead of transferring large map data structures to the GPU. This paper presents TurboMap, a GPU-parallelized and CPU-optimized local mapping backend that holistically addresses these challenges. We restructure Map Point Creation to enable parallel Keypoint Correspondence Search on the GPU, redesign and parallelize Map Point Fusion, optimize Redundant Keyframe Culling on the CPU, and integrate a fast GPU-based Local Bundle Adjustment solver. To minimize data transfer and synchronization costs, we introduce persistent GPU-resident keyframe storage. Experiments on the EuRoC and TUM-VI datasets show average local mapping speedups of 1.3x and 1.6x, respectively, while preserving accuracy.
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