Yang Xianhui, Sun Jianfeng, Ma Le, Zhou Xin, Lu Wei, Li Sining
National Key Laboratory of Laser Spatial Information, Institute of Opto-Electronic, Harbin Institute of Technology, Harbin 150001, China.
Zhengzhou Research Institute, Harbin Institute of Technology, Zhengzhou 450000, China.
Sensors (Basel). 2024 Sep 13;24(18):5950. doi: 10.3390/s24185950.
The photon-counting light laser detection and ranging (LiDAR), especially the Geiger mode avalanche photon diode (Gm-APD) LiDAR, can obtain three-dimensional images of the scene, with the characteristics of single-photon sensitivity, but the background noise limits the imaging quality of the laser radar. In order to solve this problem, a depth image estimation method based on a two-dimensional (2D) Kaniadakis entropy thresholding method is proposed which transforms a weak signal extraction problem into a denoising problem for point cloud data. The characteristics of signal peak aggregation in the data and the spatio-temporal correlation features between target image elements in the point cloud-intensity data are exploited. Through adequate simulations and outdoor target-imaging experiments under different signal-to-background ratios (SBRs), the effectiveness of the method under low signal-to-background ratio conditions is demonstrated. When the SBR is 0.025, the proposed method reaches a target recovery rate of 91.7%, which is better than the existing typical methods, such as the Peak-picking method, Cross-Correlation method, and the sparse Poisson intensity reconstruction algorithm (SPIRAL), which achieve a target recovery rate of 15.7%, 7.0%, and 18.4%, respectively. Additionally, comparing with the SPIRAL, the reconstruction recovery ratio is improved by 73.3%. The proposed method greatly improves the integrity of the target under high-background-noise environments and finally provides a basis for feature extraction and target recognition.
光子计数光探测与测距（LiDAR），特别是盖革模式雪崩光电二极管（Gm-APD）LiDAR，能够获取场景的三维图像，具有单光子灵敏度的特点，但背景噪声限制了激光雷达的成像质量。为了解决这个问题，提出了一种基于二维（2D）卡尼亚达基斯熵阈值法的深度图像估计方法，该方法将微弱信号提取问题转化为点云数据的去噪问题。利用了数据中信号峰值聚集的特性以及点云强度数据中目标图像元素之间的时空相关特征。通过在不同信背比（SBR）下进行充分的仿真和户外目标成像实验，证明了该方法在低信背比条件下的有效性。当SBR为0.025时，所提方法的目标恢复率达到91.7%，优于现有的典型方法，如峰值提取法、互相关法和稀疏泊松强度重建算法（SPIRAL），它们的目标恢复率分别为15.7%、7.0%和18.4%。此外，与SPIRAL相比，重建恢复率提高了73.3%。所提方法大大提高了高背景噪声环境下目标的完整性，最终为特征提取和目标识别提供了依据。
