Reproduce “GMMCalib: Extrinsic Calibration of LiDAR Sensors using GMM-based Joint Registration” on OpenResearch
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About this paper
Ilir Tahiraj, Felix Fent, Philipp Hafemann, Egon Ye, Markus Lienkamp
GMMCalib introduces a framework for extrinsic LiDAR sensor calibration leveraging Gaussian Mixture Model (GMM)-based joint registration. This approach enhances robustness against sensor miscalibrations and achieves higher global distance accuracy compared to traditional Iterative Closest Point (ICP) algorithms in both simulated and real-world experiments.
Key insights
- Probabilistic GMM-based joint registration intrinsically models observation noise and estimates an unbiased latent geometric shape, addressing the sensitivity and bias issues of ICP.
- The method's ability to reconstruct the calibration target's geometry enables an integrated plausibility check by comparing it with known CAD models.
- A key advantage is the joint optimization of multiple point clouds without requiring a designated reference, contributing to more consistent and robust calibration results.
Summary by alphaXiv.
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