采用传感器融合网络的单光子激光雷达成像方法

Single-photon LiDAR imaging method based on sensor fusion network

激光雷达系统采用主动照明的方式,激光发射脉冲周期信号至目标场景,激光脉冲经目标表面漫反射,由单光子雪崩二极管(Single-Photon Avalanche Diode,SPAD)探测器记录回波光子的到达时间,获得场景的深度信息。然而在探测过程中,测量结果往往会遭到环境光的干扰。传感器融合是进行单光子成像的有效方法之一。最近提出的基于LiDAR和强度相机融合的数据驱动方法大多采用扫描式激光雷达,深度获取速度慢。SPAD阵列的出现打破了帧率的限制。SPAD阵列允许同时收集多个回波光子,加速了信息采集,但分辨率较低,在探测过程中还会受到环境光的干扰,因此需要通过算法打破SPAD阵列的固有限制,从噪声中分离深度信息。针对分辨率为32×32 pixel的SPAD阵列探测器,提出了一种卷积神经网络结构,旨在强度图的引导下,将低分辨率TCSPC直方图映射至高分辨率深度图。该网络采用多尺度方法提取输入特征,并基于注意力模型融合深度数据和强度数据。另外,设计了一个损失函数组合,适用于处理TCSPC直方图数据的网络。在采集数据上进行了验证,提出方法能成功将深度数据的空间分辨率提升4倍,并在质量和数据指标上都优于其他算法。

LiDAR systems with active illumination obtain depth information of the scene using Single-Photon Avalanche Diode(SPAD)detectors to record the arrival time of reflected photons from the laser pulse.However,there is ambient light that interferes measurements during the detection period.Sensor fusion is one of the effective methods for single-photon imaging.Recently,many data-driven methods based on intensity-LiDAR fusion have achieved gratifying results,but most of them use the scanning LiDAR which has a slow depth acquisition speed.The advent of the SPAD array can overcome the limitation of frame rates.The SPAD array allows the collection of multiple returned photons at the same time,which accelerates the information collection process.However,the spatial resolution of SPAD array detectors is typically low,and the detection process is also interfered by the ambient light.Therefore,it is necessary to break the inherent limitation of the SPAD array through an algorithm to separate the depth information from the noise.In this paper,for the SPAD array detector with the array size of 32×32 pixel,a convolutional neural network was proposed,which could reconstruct high resolution clean TCSPC histogram under the guidance of the intensity image.A multi-scale approach was adopted to extract input features,and the fusion of depth data and intensity data was further processed based on the attention mechanism in the network.In addition,a loss function combination suitable for the TCSPC histogram data processing network was designed,where the overall distribution of photons and the ordinal relationship between time bins in the temporal dimension could be simultaneously considered.The method proposed in this paper can successfully increase the depth spatial resolution by 4 times,and the efficacy of proposed method is verified on realistic data,which is superior to state-of-the-art methods qualitatively and quantitatively.