High-Speed Stereo Visual SLAM for Low-Powered Computing Devices
<|reference_start|>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 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).<|reference_end|>
@article{kumar2024high-speed,
    title={High-Speed Stereo Visual SLAM for Low-Powered Computing Devices},
    author={Ashish Kumar, Jaesik Park, Laxmidhar Behera},
    journal={IEEE Robotics & Automation Letters, 2023},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04090},
    primaryClass={cs.RO cs.CV}
}