Improvement to LiDAR-camera extrinsic calibration by using 3D–3D correspondences

Duy, An Nguyen; Minh, Tri Nguyen; Yoo, Myungsik

doi:10.1016/j.ijleo.2022.168917

Cited by 4 publications

(1 citation statement)

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“…The external parameters are solved iteratively using a 3D-3D point matching approach (Nguyen et al, 2022), which involves matching feature points' coordinates P i L in the LiDAR coordinate system with their coordinates P i C in the camera coordinate system. The objective optimization function is represented as equation ( 5):…”

Section: Sensor Fusion Calibration 221 Principle Of Sensor Fusion Cal...mentioning

confidence: 99%

Extrinsic calibration method for integrating infrared thermal imaging camera and 3D LiDAR

Zhang,

Yuan,

Meng

et al. 2024

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Purpose In the context of fire incidents within buildings, efficient scene perception by firefighting robots is particularly crucial. Although individual sensors can provide specific types of data, achieving deep data correlation among multiple sensors poses challenges. To address this issue, this study aims to explore a fusion approach integrating thermal imaging cameras and LiDAR sensors to enhance the perception capabilities of firefighting robots in fire environments. Design/methodology/approach Prior to sensor fusion, accurate calibration of the sensors is essential. This paper proposes an extrinsic calibration method based on rigid body transformation. The collected data is optimized using the Ceres optimization algorithm to obtain precise calibration parameters. Building upon this calibration, a sensor fusion method based on coordinate projection transformation is proposed, enabling real-time mapping between images and point clouds. In addition, the effectiveness of the proposed fusion device data collection is validated in experimental smoke-filled fire environments. Findings The average reprojection error obtained by the extrinsic calibration method based on rigid body transformation is 1.02 pixels, indicating good accuracy. The fused data combines the advantages of thermal imaging cameras and LiDAR, overcoming the limitations of individual sensors. Originality/value This paper introduces an extrinsic calibration method based on rigid body transformation, along with a sensor fusion approach based on coordinate projection transformation. The effectiveness of this fusion strategy is validated in simulated fire environments.

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Section: Sensor Fusion Calibration 221 Principle Of Sensor Fusion Cal...mentioning

confidence: 99%