80 × 120 AI-enhanced LiDAR system based on a lightweight intensity–RGB–dToF sensor fusion neural network deployed on an edge device

作者：Lebei Cui, Jie Li, Shenglong Zhuo, Yifan Wu, Sifan Zhou, Jian Qian, Miao Sun, Jier Wang, Patrick Yin Chiang, Yun Chen · 发表于：Optics Letters · 年份：2023 · DOI：10.1364/ol.504351 · 被引用次数：4 · 研究领域：Advanced Optical Sensing Technologies、Remote Sensing and LiDAR Applications、Optical measurement and interference techniques

Collecting higher-quality three-dimensional points-cloud data in various scenarios practically and robustly has led to a strong demand for such dToF-based LiDAR systems with higher ambient noise rejection ability and limited optical power consumption, which is a sharp conflict. To alleviate such a clash, an idea of utilizing a strong ambient noise rejection ability of intensity and RGB images is proposed, based on which a lightweight CNN is newly, to the best of our knowledge, designed, achieving a state-of-the-art performance even with 90 × less inference time and 480 × fewer FLOPs. With such net deployed on edge devices, a complete AI-LiDAR system is presented, showing a 100 × fewer signal photon demand in simulation experiments when creating depth images of the same quality.