Automatic camera and range sensor calibration using a single shot

By Andreas Geiger, Frank Moosmann, Omer Car, Bernhard Schuster. IEEE International Conference on Robotics and Automation, 2012

656 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/140a6bdfb0564eb18a1f51a39dff36f20272a461

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration (2016, neutral): “Given cross-modality data acquired from different sensors with complementary information, registration can be used to fuse the data [7], [8] or determine the relative poses between these sensors [9], [10].”
The Multivehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception (2018, methodology): “The transformation that takes a point from the lidar frame to the left DAVIS frame was initially calibrated using the Camera and Range Calibration Toolbox [33].”
Automatic Online Calibration of Cameras and Lasers (2013, methodology): “Theirs requires human labeling of correspondences between laser points and camera pixels, and then computes the best calibration using the perspective-from-n-points (PnP) algorithm.”
KAIST Multi-Spectral Day/Night Data Set for Autonomous and Assisted Driving (2018, methodology): “[14] used multiple chess boards or a triaxial calibration target to find the geometric relationship between heterogeneous sensors.”
Adversarial T-Shirt! Evading Person Detectors in a Physical World (2019, methodology): “, where each intersection between two checkerboard grid regions is selected as a control point. We remark that the considered control points can be accurately detected using the Matlab vision toolbox [30], and the videos used to generate TPS transformations are independent of testing data for evalua”
Vision meets robotics: The KITTI dataset (2013, methodology): “For details about the benchmarks and evaluation metrics we refer the reader to Geiger et al. (2012a).”
A new performance measure and evaluation benchmark for road detection algorithms (2013, methodology): “Per frame we recover the homography between the image and the road plane using RANSAC plane fitting on the 3D measurements of a Velodyne laser scanner which has been calibrated with respect to the cameras [29].”