Time of flight (TOF) based light detection and ranging (LiDAR) is a technology for calculating distance between start/stop signals of time of flight. In lab-built LiDAR, two ranging systems for measuring flying time between start/stop signals include time-to-digital converter (TDC) that counts time between trigger signals and analog-to-digital converter (ADC) that processes the sampled start/stop pulses waveform for time estimation. We study the influence of waveform characteristics on range accuracy and precision of two kinds of ranging system. Comparing waveform based ranging (WR) with analog discrete return system based ranging (AR), a peak detection method (WR-PK) shows the best ranging performance because of less execution time, high ranging accuracy, and stable precision. Based on a novel statistic mathematical method maximal information coefficient (MIC), WR-PK precision has a high linear relationship with the received pulse width standard deviation. Thus keeping the received pulse width of measuring a constant distance as stable as possible can improve ranging precision.

The fast estimation of blue-green laser transmission characteristics through the fluctuating sea surface, such as refraction angles and transmittance, is very important to correct operating parameters, detection depth and anti-detection warning in airborne Light Detection and Ranging (LiDAR) applications. However, the geometry of the sea surface is changed by complex environment factors, such as wind and wave, which significantly affect the rapid acquisition of the blue-green laser transmission characteristics. To address this problem, a fast analysis method is provided to rapidly compute the blue-green laser transmittance and refraction angles through the fluctuating sea surface driven by different wind directions and speeds. In the method, a three-dimensional wave model driven by the wind was built to describe the wave spatial distribution varying with time. Using the wave model, the propagation path of the scanning laser footprint was analyzed using the proposed meshing method, thus the transmittance and refraction angles of the optical path can be fast obtained by using parallel computing. The simulation results imply that the proposed method can reduce the time consumption by 70% compared with the traditional analytical method with sequential computing. This paper provides some statistical laws of refraction angles and transmittance through the fluctuating sea surface under different wind conditions, which may serve as a basic for fast computation of airborne LiDAR transmission characteristics in complex environments.

To achieve online multi-target laser ranging in full-waveform light detection and ranging (LiDAR), a multi-target laser ranging method using waveform decomposition is proposed and implemented on a field programmable gate array (FPGA). In this method, an emitted laser pulse and the laser echo scattered from multiple targets are decomposed into a set of Gaussian functions, then the distances between the LiDAR and the targets are obtained using the waveform parameters of Gaussian functions. The waveform decomposition method is modified and implemented on a FPGA for online laser ranging. Simulations and experiments were conducted to verify the ranging performances of the proposed method by the comparition with those of a post-processing Gauss-Newton (post-GN) method. The results of simulations show that the proposed method is more efficient compared with the post-GN method. The time cost reduces 79.8% to 91.1% for a laser echo scattered from three close targets. In experiments, a lab-built full-waveform LiDAR system was used to obtain the waveforms of emitted laser pulses and laser echoes scattered from 1, 2, or 3 targets. The results show that the ranging performances of the proposed method are comparable with those of the post-GN method. The proposed method is capable to distinguish close targets with an equivalent time space of 1.8 times pulse width of an emitted laser pulse and decompose laser echo with a signal-to-noise ratio (SNR) higher than 20 dB. The results show that the proposed method can obtain distances of multiple close targets from a laser echo efficiently and accurately.

The generation of dense and accurate point clouds from airborne light detection and ranging (LiDAR) waveform data is crucial to forest inventory. This work proposes a deconvolution method with: 1) an automatic stopping criterion to differentiate near-adjacent targets and 2) an iterative false subwaveform removal algorithm to remove outliers caused by noise. Synthetic waveforms with different overlap rates were processed using the proposed method, the Gaussian decomposition (GD) method, and the Richardson Lucy (RL) deconvolution method. Results showed that: 1) the number of subwaveforms detected by the proposed method is 9% higher than that of the RL and 20% higher than that of the GD when the overlap rates are larger than 0.6 and 2) the proposed method is of the smallest ground and peak distance errors. Results of the indoor experiment also show that the proposed method is superior in finding near targets meanwhile leading to small ground and peak distance error. Furthermore, the proposed method was tested by airborne waveforms from the Dagujia forest farm. The point cloud density acquired by the proposed method is 3% and 35% higher than that by the RL and GD method. Fewer outliers are produced by the proposed method. The number of individual trees extracted from the proposed point clouds is 22%, 51%, and 57% greater than those extracted from the RL, the GD, and the reference point clouds using the canopy height model-based method. Best individual tree extraction result is produced by the proposed method, especially for an area with small trees.

To increase the measurement rate of real-time in-situ laser ranging, an online Gauss-Newton-based parallel-pipeline method (GNPPo) is proposed to extract waveform parameters. The proposed method includes waveform pre-processing for initial estimation, variable substitution for fast computation, and Gauss-Newton method for optimization. In waveform pre-processing, parallel-pipeline architecture was used to obtain initial values of waveform parameters as input for Gauss-Newton method. In the variable substitution, the initial values were transformed to reduce the number of divisions and accelerate Jacobi matrix calculation. To obtain the optimized waveform parameters, adjoint matrix method was used to calculate the iteration vector in Gauss-Newton method. The optimized waveform parameters required inverse transformation by variable substitution for accurately calculating the distance. The proposed method employed parallel-pipeline architecture, implemented on Kintex-7 FPGA. Simulations first proved that the proposed method decreased time cost and increased the occupied computational resource. Extensive experiments were conducted using a lab-built full-waveform ranging system. In comparison with the online Gauss-Newton-based pipeline method (GNPo) and the Gauss-Newton-based post-processing method (GNp), experimental results revealed that the proposed method realizes a range measurement rate of 285.7 kHz, approximately 2.5 times and 49 times as fast as GNPo and GNp, respectively. The mean range error and range standard deviation of GNPPo are 0.6 cm and 1.7 cm, at a distance of 21 m with an SNR of 36.6 dB, roughly the same scale of GNp, and a millimeter-scale increase of GNPo. The proposed method will be used for LiDAR application on autonomous vehicles with real-time centimeter accuracy.