FAREC: few accumulations range extraction and correction in high-flux single-photon Lidar

Single-photon Lidar (SPL) has many applications, ranging from geographic surveying to autonomous navigation. For accurate range extraction and range-walk-error (RWE) correction, a good TCSPC histogram is usually required and collected by using thousands of accumulations. While in high-flux regime, using a few accumulations is possible to perform range extraction and RWE correction and could speed up data acquisition process. This paper proposed a method to deal with two challenges of depth estimation when only a few accumulations (less than 10 times) are applied, including the extreme sparsity of photon-counting data and the non-steady state in free-running SPL. We first developed a forward model for non-steady state free-running SPL. As a second step, we computed the initial depth through our coarse depth estimation method, which incorporated the maximum of cross-correlation, threshold filtering, and cluster analysis of potential range candidates. Then solving an inverse problem through our forward model and initial values gives the estimate of the depth. Monte Carlo simulated results indicated good improvement in performance of detection probabilities and RWE correction. As compared with a baseline method, our method maintains a same detection probability while shows a 26% less false alarm probability on average. When signal-background ratio SBR is 0.2 (S = 2, B = 10) and accumulating times is only 4, our method gives a 75% less RWE. Our method could enable improvement of depth estimation from sparse photon-counting data collected by a few accumulations in SPL.