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

Ashish Kumar, Jaesik Park, Laxmidhar Behera

Abstract: 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.