CalibDNN: Multimodal Sensor Calibration for Perception Using Deep Neural Networks

• URL: http://arxiv.org/abs/2103.14793v1
• Date: Sat, 27 Mar 2021 02:43:37 GMT
• Title: CalibDNN: Multimodal Sensor Calibration for Perception Using Deep Neural Networks
• Authors: Ganning Zhao, Jiesi Hu, Suya You and C.-C. Jay Kuo
• Abstract summary: We propose a novel deep learning-driven technique (CalibDNN) for accurate calibration among multimodal sensor, specifically LiDAR-Camera pairs. 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.
• Score: 27.877734292570967
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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