在低功耗设备上实现60帧以上实时立体视觉定位，精度高且速度快。
High-Speed Stereo Visual SLAM for Low-Powered Computing Devices
- 采用受限校正技术提升角点检测准确性，减少误检。
- 通过金字塔裁剪与聚合算法，实现在嵌入式设备上超过60FPS的处理速度。
- 专为资源受限设备优化，适合移动机器人与无人机应用。
我们提出一种高精度、支持GPU加速的立体视觉SLAM系统Jetson-SLAM。该系统在NVIDIA低功耗10W Jetson-NX嵌入式设备上实现超过60FPS的帧处理速率，在桌面级200W GPU上更达200FPS以上，即使在立体配置和多尺度设置下依然保持高性能。主要贡献包括：(i) 提出有界校正技术，避免FAST检测中将大量非角点误判为角点，显著提升定位精度；(ii) 设计新型金字塔裁剪与聚合（PyCA）算法，结合多位置每线程裁剪（MLPT）与线程高效波段分配（TEWA）策略，充分利用GPU并行能力，使系统在嵌入式设备上兼顾高精度与高速率；(iii) 通过数据共享机制实现资源高效，降低内存开销。在KITTI、EuRoC、KAIST-VIO三个挑战性数据集及Full-BA、ICE-BA两种高精度后端验证下，Jetson-SLAM成为当前最快且准确的GPU加速SLAM系统。
原文摘要 · Abstract (English)
We present an accurate and GPU-accelerated Stereo Visual SLAM design called Jetson-SLAM. It exhibits frame-processing rates above 60FPS on NVIDIA's low-powered 10W Jetson-NX embedded computer and above 200FPS on desktop-grade 200W GPUs, even in stereo configuration and in the multiscale setting. Our contributions are threefold: (i) a Bounded Rectification technique to prevent tagging many non-corner points as a corner in FAST detection, improving SLAM accuracy. (ii) A novel Pyramidal Culling and Aggregation (PyCA) technique that yields robust features while suppressing redundant ones at high speeds by harnessing a GPU device. PyCA uses our new Multi-Location Per Thread culling strategy (MLPT) and Thread-Efficient Warp-Allocation (TEWA) scheme for GPU to enable Jetson-SLAM achieving high accuracy and speed on embedded devices. (iii) Jetson-SLAM library achieves resource efficiency by having a data-sharing mechanism. Our experiments on three challenging datasets: KITTI, EuRoC, and KAIST-VIO, and two highly accurate SLAM backends: Full-BA and ICE-BA show that Jetson-SLAM is the fastest available accurate and GPU-accelerated SLAM system (Fig. 1).
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