Online Gauss–Newton-Based Parallel-Pipeline Method for Real-Time In-Situ Laser Ranging

Abstract

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.