High-Speed Stereo Visual SLAM for Low-Powered Computing Devices

We present an accurate and GPU-accelerated Stereo Visual SLAM design called Jetson-SLAM. It exhibits frame-processing rates above
$\mathbf {60}$
FPS on NVIDIA's low-powered
$\mathbf {10}$
W Jetson-NX embedded computer and above
$\mathbf {200}$
FPS on desktop-grade
$\mathbf {200}$
W 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).
Fig. 1.

(a) Output of Jetson-SLAM's GPU-accelerated and resource-efficient Frontend–Middle-end design, (b) the output trajectory, (c) Frames-Per-Second benchmarking on Jetson-NX embedded computer, and (d) SLAM performance on a KITTI sequence.