CalibDB: Diverse Multimodal Calibration Benchmark

Justin Yue    Ayoub Elidrissi    Divyank Shah    Jerin Peter    Konstantinos Karydis    Hang Qiu   

University of California, Riverside   

In submission, RSS 2025 Demo

CalibDB is a diverse and challenging multi-modal calibration dataset and benchmark. The dataset contains various discrete and continous traces from cameras and LiDARs, which are placed in different poses with dynamic extrinsics via robotic arms manipulation. The benchmark evaluates the state-of-the-art multi-modal calibration methods, which demonstrates the research gaps and the challenges existing methods face. The proposed pipeline and dataset pave the way for the community to develop more accurate, robust, domain-transferable multimodal calibration methods.
CalibDB Intro


CalibDB Overview

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Qualitative Results

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Citation

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