Abstract
3D Lidar imaging can be a challenging modality when using multiple wavelengths, or when imaging in high noise environments (e.g., imaging through obscurants). This paper presents a hierarchical Bayesian algorithm for the robust reconstruction of multispectral single-photon Lidar data in such environments. The algorithm exploits multi-scale information to provide robust depth and reflectivity estimates together with their uncertainties to help with decision making. The proposed weight-based strategy allows the use of available guide information that can be obtained by using state-of-the-art learning based algorithms. The proposed Bayesian model and its estimation algorithm are validated on both synthetic and real images showing competitive results regarding the quality of the inferences and the computational complexity when compared to the state-of-the-art algorithms.
Highlights
T HREE-DIMENSIONAL (3D) imaging has generated significant interest from the scientific community due to its increasing use in applications such as self-driving autonomous vehicles [1], [2]
Single-photon light detection and ranging (Lidar) is an approach used for high resolution 3D imaging, where its high sensitivity and excellent surface-to-surface resolution can provide rich information on the depth profile and reflectivity of observed targets in challenging imaging scenarios
Single-photon Lidar operates by emitting picosecond duration laser pulses and collecting the reflected photons using a singlephoton sensitive detector which measures the arrival time of each
Summary
T HREE-DIMENSIONAL (3D) imaging has generated significant interest from the scientific community due to its increasing use in applications such as self-driving autonomous vehicles [1], [2]. This paper combines the advantages of these families by proposing a principled statistical-based algorithm, that can use state-of-the-art algorithms as a guide for robust processing of multispectral 3D Lidar data acquired through obscurants. An approximate likelihood distribution is considered and a hierarchical Bayesian model is proposed to exploit the data Poisson statistics, the multi-scale information (known to improve noise and photon-sparsity robustness [19], [25], [33], [37]), and prior knowledge on the depth and reflectivity maps. This hierarchical model ensures the robustness of the proposed strategy to the mismatch between the simplified observation model and the actual one.
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