Photometric LiDAR and RGB-D Bundle Adjustment

Giammarino, Luca Di; Giacomini, Emanuele; Brizi, Leonardo; Salem, Omar M.; Grisetti, Giorgio

doi:10.1109/lra.2023.3281907

Cited by 5 publications

(1 citation statement)

References 28 publications

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“…Current research shows that the LiDAR-RGB combination may be fused together using guided depth completionapproaches to provide a unified dense RGB-D representation, which is widely employed for perception applications [1][2][3][4]. Furthermore, in the field of 3D reconstruction, recent findings show that coupling the two sensors may lead to a more robust and accurate estimate [5].…”

Section: Introductionmentioning

confidence: 99%

Ca2Lib: Simple and Accurate LiDAR-RGB Calibration Using Small Common Markers

Giacomini,

Brizi,

Di Giammarino

et al. 2024

Sensors

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Modern visual perception techniques often rely on multiple heterogeneous sensors to achieve accurate and robust estimates. Knowledge of their relative positions is a mandatory prerequisite to accomplish sensor fusion. Typically, this result is obtained through a calibration procedure that correlates the sensors’ measurements. In this context, we focus on LiDAR and RGB sensors that exhibit complementary capabilities. Given the sparsity of LiDAR measurements, current state-of-the-art calibration techniques often rely on complex or large calibration targets to resolve the relative pose estimation. As such, the geometric properties of the targets may hinder the calibration procedure in those cases where an ad hoc environment cannot be guaranteed. This paper addresses the problem of LiDAR-RGB calibration using common calibration patterns (i.e., A3 chessboard) with minimal human intervention. Our approach exploits the flatness of the target to find associations between the sensors’ measurements, leading to robust features and retrieval of the solution through nonlinear optimization. The results of quantitative and comparative experiments with other state-of-the-art approaches show that our simple schema performs on par or better than existing methods that rely on complex calibration targets.

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