Integrated adaptive coherent LiDAR for 4D bionic vision
Abstract
Light detection and ranging (LiDAR) is a ubiquitous tool to provide precise spatial awareness in various perception environments. A bionic LiDAR that can mimic human-like vision by adaptively gazing at selected regions of interest within a broad field of view is crucial to achieve high-resolution imaging in an energy-saving and cost-effective manner. However, current LiDARs based on stacking fixed-wavelength laser arrays and inertial scanning have not been able to achieve the desired dynamic focusing patterns and agile scalability simultaneously. Moreover, the ability to synchronously acquire multi-dimensional physical parameters, including distance, direction, Doppler, and color, through seamless fusion between multiple sensors, still remains elusive in LiDAR. Here, we overcome these limitations and demonstrate a bio-inspired frequency-modulated continuous wave (FMCW) LiDAR system with dynamic and scalable gazing capability. Our chip-scale LiDAR system is built using hybrid integrated photonic solutions, where a frequency-chirped external cavity laser provides broad spectral tunability, while on-chip electro-optic combs with elastic channel spacing allow customizable imaging granularity. Using the dynamic zoom-in capability and the coherent FMCW scheme, we achieve a state-of-the-art resolution of 0.012 degrees, providing up to 15 times the resolution of conventional 3D LiDAR sensors, with 115 equivalent scanning lines and 4D parallel imaging. We further demonstrate cooperative sensing between our adaptive coherent LiDAR and a camera to enable high-resolution color-enhanced machine vision.
Cite
@article{arxiv.2410.08554,
title = {Integrated adaptive coherent LiDAR for 4D bionic vision},
author = {Ruixuan Chen and Yichen Wu and Ke Zhang and Chuxin Liu and Yikun Chen and Wencan Li and Bitao Shen and Zhaoxi Chen and Hanke Feng and Zhangfeng Ge and Yan Zhou and Zihan Tao and Weihan Xu and Yimeng Wang and Pengfei Cai and Dong Pan and Haowen Shu and Linjie Zhou and Cheng Wang and Xingjun Wang},
journal= {arXiv preprint arXiv:2410.08554},
year = {2024}
}