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New method jointly calibrates camera-LiDAR without targets

Researchers have developed a novel pipeline for calibrating cameras and LiDAR sensors without requiring physical targets. This method jointly estimates both the intrinsic parameters of the camera, including distortion, and the extrinsic parameters that define the relative position and orientation between the camera and LiDAR. The approach leverages deep learning for pixel-point correspondences and integrates this with a nonlinear optimization process to refine both sets of calibration parameters simultaneously. AI

IMPACT Introduces a more robust and automated method for sensor calibration, crucial for autonomous systems.

RANK_REASON The cluster contains a research paper detailing a new methodology for sensor calibration.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Simon Bultmann, Daniele Cattaneo, Abhinav Valada ·

    Joint Target-Less Intrinsic and Extrinsic Camera-LiDAR Calibration using Deep Point Correspondences

    arXiv:2605.23397v1 Announce Type: new Abstract: Accurate camera-LiDAR calibration is a prerequisite for robust multi-modal perception in robotics. Recent target-less approaches based on deep point correspondences achieve remarkable performance for extrinsic calibration but assume…

  2. arXiv cs.CV TIER_1 English(EN) · Abhinav Valada ·

    Joint Target-Less Intrinsic and Extrinsic Camera-LiDAR Calibration using Deep Point Correspondences

    Accurate camera-LiDAR calibration is a prerequisite for robust multi-modal perception in robotics. Recent target-less approaches based on deep point correspondences achieve remarkable performance for extrinsic calibration but assume rectified images with known intrinsics. In this…