228 × 304 200mW激光雷达基于一个单点全球深度d-ToF传感器和RGB引导超分辨率神经网络
Optics letters
|June 30, 2023
概括
本研究介绍了一种使用软件和硬件共同设计的新型激光雷达系统,用于改进RGB-D融合. 新系统提高了深度地图的分辨率和准确性,克服了当前成像技术的局限性.
科学领域:
- 计算机视觉和成像系统计算机视觉和成像系统
- 嵌入式系统和硬件加速器
背景情况:
- 现有的成像系统面临着低分辨率和高功耗的挑战,阻碍了RGB-D融合算法.
- 将深度地图分辨率与RGB传感器对齐对于实际的3D成像应用至关重要.
研究的目的:
- 通过软件和硬件共同设计,使用单眼RGB 3D成像算法实现激光雷达系统.
- 为了解决当前成像系统中低输出分辨率和高功耗的局限性.
主要方法:
- 集成一个6.4 × 6.4-mm2的深度学习加速器 (DLA) 系统上芯片 (SoC) (40nm CMOS).
- 整合一个3.6mm2的TX-RX集成芯片 (180nm CMOS) 用于定制的单像素成像神经网络.
- 对于激光雷达系统的软件和硬件共同设计方法.
主要成果:
- 与RGB-only单眼深度估计相比,根的平均平方误差从0.48m减少到0.3m.
- 实现了与RGB输入分辨率匹配的输出深度图分辨率.
- 在评估的数据集上表现得更好.
结论:
- 开发的激光雷达系统有效地克服了分辨率和功耗方面的挑战.
- 软件和硬件的共同设计使得高性能单眼RGB 3D成像解决方案成为可能.
- 该系统为深度估计任务提供了更高的精度和分辨率.
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