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).

翻译：我们提出了一种精确且GPU加速的双目视觉SLAM设计，称为Jetson-SLAM。该系统在NVIDIA低功耗10W的Jetson-NX嵌入式计算机上实现了超过60FPS的帧处理速率，在桌面级200W GPU上甚至超过200FPS，即使在双目配置和多尺度设置下也是如此。我们的贡献有三方面：(i) 一种有界校正技术，用于防止在FAST检测中将大量非角点标记为角点，从而提高了SLAM的精度。(ii) 一种新颖的金字塔式剔除与聚合技术，通过利用GPU设备，在高速处理的同时产生鲁棒的特征并抑制冗余特征。PyCA采用我们新的多位置每线程剔除策略和线程高效线程束分配方案，使Jetson-SLAM能够在嵌入式设备上实现高精度和高速度。(iii) Jetson-SLAM库通过数据共享机制实现了资源高效利用。我们在三个具有挑战性的数据集：KITTI、EuRoC和KAIST-VIO，以及两个高精度SLAM后端：Full-BA和ICE-BA上进行的实验表明，Jetson-SLAM是目前可用的最快、精确且GPU加速的SLAM系统（图1）。