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A Joint Optimization Approach of LiDAR-Camera Fusion for Accurate Dense 3D Reconstructions

Weikun Zhen, Yaoyu Hu, Jingfeng Liu and Sebastian Scherer
Journal Article, IEEE Robotics and Automation Letters, Vol. 4, No. 4, pp. 3585 - 3592, July, 2019

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Abstract

Fusing data from LiDAR and camera is conceptually attractive because of their complementary properties. For instance, camera images are higher resolution and have colors, while LiDAR data provide more accurate range measurements and have a ​​wider Field Of View (FOV). However, the sensor fusion problem remains challenging since it is difficult to find reliable correlations between data of very different characteristics (geometry vs. texture, sparse vs. dense). This paper proposes an offline LiDAR-camera fusion method to build dense, accurate 3D model​​s. Specifically, our method jointly solves a bundle adjustment (BA) problem and a cloud registration problem to compute camera poses and the sensor extrinsic calibration. In experiments, we show that our method can achieve an averaged accuracy of 2.7mm and resolution of 70 points/cm2 by comparing to the ground truth data from a survey scanner. Furthermore, the extrinsic calibration result is discussed and shown to outperform the state-of-the-art method.


@article{Zhen-2019-117826,
author = {Weikun Zhen and Yaoyu Hu and Jingfeng Liu and Sebastian Scherer},
title = {A Joint Optimization Approach of LiDAR-Camera Fusion for Accurate Dense 3D Reconstructions},
journal = {IEEE Robotics and Automation Letters},
year = {2019},
month = {July},
volume = {4},
number = {4},
pages = {3585 - 3592},
keywords = {Sensor Fusion, Mapping, Calibration and Identification},
} 2019-09-19T10:47:24-04:00