Time-of-flight (TOF) Lidar is widely used in capturing three-dimensional (3D) structure and reflectivity information. For using Geiger-mode avalanche photodiode (Gm-APD) and the technique time correlated single photon counting (TCSPC), a direct-detection 3D imaging lidar has high sensitivity in low-light-level (LLL) scene. Traditional method needs long fixed dwell time to collect tens of thousands of photons to find accurate range and mitigate Poisson noise at each pixel. We present a method that acquires accurate depth and intensity images using a small amount of detected echo photons and having quantitative analysis to estimate whether results are in the confidence interval. Based on prior knowledge that the echo signal is in the shape of emitted laser, we use one or two orders of magnitude back-reflected photons less than traditional method, fitting a curve of laser-return pulse by nonlinear least-squares fitting in order to obtain the range. The condition of moving to next pixel in our method is acquiring a fixed number of back-reflected photons, instead of sampling for a fixed time. This adaptive jump condition is able to speed up the scanning without more distortion. The results are analyzed with chi-square test to determine if the curve we fit has enough credibility. This quantitative analysis provides an important judgment condition for our method of fitting curve to recover the depth image. Experimental results demonstrate that our method is able to obtain the millimeter accuracy depth image in the confidence interval using hundreds of photons and increases photon-efficiency more than 10-fold over traditional method. Thus our method will be useful in LLL scene, such as military reconnaissance and remote sensing.
For an unknown characteristic target scene, the laser radar system that uses single-photon detector cannot directly estimate the dwell time of every pixel. Therefore, as the difference of target reflectivity, depth estimation appears inadequate sampling or redundant sampling in the conventional imaging method of maximum likelihood estimation (MLE-CIM). In this work, an adaptive depth imaging method (ADIM) is presented. ADIM is capable to obtain the depth estimation of target and adaptively decide the dwell time of each pixel. The experimental results reveal that ADIM can accurately obtain the 3D depth image of target even at the condition of low signal-to-noise ratio.
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