This paper addresses the problem of joint surface detection and depth estimation from single-photon Lidar (SPL) data. Traditional 3D ranging methods for SPL usually perform surface detection and range estimation sequentially to alleviate the computational burden of joint detection and estimation. Adopting a Bayesian formalism, the joint detection/estimation problem is formulated as a single inference problem. To avoid the intractable integrals usually involved with variable marginalization, we consider discrete variables and the resulting problem is recast as a model selection/averaging problem.We illustrate our method for a case where the expected signal-to-background (e.g., the target reflectivity and ambient illumination level) is unknown but the proposed framework can be adapted to more complex problems where the target depth can be obtained by combining several estimators. We demonstrate the additional benefits of the proposed method in also providing a conservative approach to uncertainty quantification of the calculated depth estimates, which can be used for real time analysis. The benefits of the proposed methods are illustrated using synthetic and real SPL data for targets at up to 8.6 km.
|Title of host publication||2021 Sensor Signal Processing for Defence Conference (SSPD)|
|Publication status||Published - 23 Sept 2021|
|Event||10th Sensor Signal Processing for Defence Conference 2021 - Edinburgh, United Kingdom|
Duration: 14 Sept 2021 → 15 Sept 2021
|Conference||10th Sensor Signal Processing for Defence Conference 2021|
|Abbreviated title||SSPD 2021|
|Period||14/09/21 → 15/09/21|
- Bayesian estimation
- Ensemble estimation.
- Single-photon Lidar
ASJC Scopus subject areas
- Signal Processing
- Safety, Risk, Reliability and Quality