Automatic Targetless Extrinsic Calibration of a 3D Lidar and Camera by Maximizing Mutual Information

By Gaurav Pandey, James R. McBride, Silvio Savarese, Ryan M. Eustice. AAAI Conference on Artificial Intelligence, 2012

300 citations recorded by the source corpus.

This paper reports on a mutual information (MI) based algorithm for automatic extrinsic calibration of a 3D laser scanner and optical camera system. By using MI as the registration criterion, our method is able to work in situ without the need for any specific calibration targets, which makes it practical for in-field calibration. The calibration parameters are estimated by maximizing the mutual information obtained between the sensor-measured surface intensities. We calculate the Cramer-Rao-Lower-Bound (CRLB) and show that the sample variance of the estimated parameters empirically approaches the CRLB for a sufficient number of views. Furthermore, we compare the calibration results to independent ground-truth and observe that the mean error also empirically approaches to zero as the number of views are increased. This indicates that the proposed algorithm, in the limiting case, calculates a minimum variance unbiased (MVUB) estimate of the calibration parameters. Experimental results are presented for data collected by a vehicle mounted with a 3D laser scanner and an omnidirectional camera system.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/f3ce6e501e20d52e1d5c6d822e08db034fbb3488

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Citations in the corpus

Automatic Registration of RGB-D Scans via Salient Directions (2013, methodology): “This registration can be performed by targets or calibration patterns [21] or by maximizing mutual information between reflectance and color [17].”
Pixel-Level Extrinsic Self Calibration of High Resolution LiDAR and Camera in Targetless Environments (2021, methodology): “Extracting features on the projected point clouds and match them to the image features, such as [13], would suffer from these fundamental problems and cause significant errors in edge extraction and calibration.”
Visual localization within LIDAR maps for automated urban driving (2014, methodology): “More recently, the idea has been transferred to robotics for calibration of visual cameras to LIDAR scanners [15], [16].”