CalibDNN: multimodal sensor calibration for perception using deep neural networks

Ganning Zhao; Jiesi Hu; Suya You; C.-C. Jay Kuo

doi:10.1117/12.2587994

12 April 2021 CalibDNN: multimodal sensor calibration for perception using deep neural networks

Ganning Zhao, Jiesi Hu, Suya You, C.-C. Jay Kuo

Proceedings Volume 11756, Signal Processing, Sensor/Information Fusion, and Target Recognition XXX; 117561D (2021) https://doi.org/10.1117/12.2587994
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Current perception systems often carry multimodal imagers and sensors such as 2D cameras and 3D LiDAR sensors. To fuse and utilize the data for downstream perception tasks, robust and accurate calibration of the multimodal sensor data is essential. We propose a novel deep learning-driven technique (CalibDNN) for accurate calibration among multimodal sensor, specifically LiDAR-Camera pairs. The key innovation of the proposed work is that it does not require any specific calibration targets or hardware assistants, and the entire processing is fully automatic with a single model and single iteration. Results comparison among different methods and extensive experiments on different datasets demonstrates the state-of-the-art performance.

Conference Presentation

Citation Download Citation

Ganning Zhao, Jiesi Hu, Suya You, and C.-C. Jay Kuo "CalibDNN: multimodal sensor calibration for perception using deep neural networks", Proc. SPIE 11756, Signal Processing, Sensor/Information Fusion, and Target Recognition XXX, 117561D (12 April 2021); https://doi.org/10.1117/12.2587994

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