Unified temporal and spatial calibration for multi-sensor systems

By Paul Timothy Furgale, J. Rehder, Roland Yves Siegwart. 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013

812 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/120ff265db7c9fcef97a5c99c0821d59196a1672

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Citations in the corpus

Keyframe-Based Visual-Inertial SLAM using Nonlinear Optimization (2013, methodology): “calibration of the camera with respect to the IMU using [4] was available beforehand.”
The Multivehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception (2018, methodology): “The camera intrinsics, stereo extrinsics, and camera-IMU extrinsics are calibrated using the Kalibr toolbox3 [30], [31], [32], the extrinsics between the left DAVIS camera and Velodyne”
Direct visual-inertial odometry with stereo cameras (2016, methodology): “For all other datasets an offline calibration was performed using the Kalibr framework [11].”
Are We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM (2019, methodology): “Sensors Tool D435i T265 Kalibr [13] github.”
DSEC: A Stereo Event Camera Dataset for Driving Scenarios (2021, methodology): “trinsic and extrinsic calibration procedure uses E2VID, a neural network-based image reconstruction [40], in combination with Kalibr [41].”
On-Manifold Preintegration for Real-Time Visual--Inertial Odometry (2015, methodology): “1We calibrate the IMU-camera delay using the Kalibr toolbox [59].”
Visual-Inertial Monocular SLAM With Map Reuse (2016, background): “Camera and IMU are considered rigidly attached and the transformation TCB = [RCB|CpB] between their reference systems known from calibration [19].”
Canadian Adverse Driving Conditions dataset (2020, methodology): “In order to determine the transformation from camera to IMU, we use the well-established Kalibr package (Furgale et al., 2013).”
The event-camera dataset and simulator: Event-based data for pose estimation, visual odometry, and SLAM (2016, methodology): “This temporal shift is also reported by Kalibr.”
SVO: Semidirect Visual Odometry for Monocular and Multicamera Systems (2017, background): “Sparse image alignment seeks to ﬁnd T k,k − 1 that minimizes the photometric difference between image patches corresponding to the same 3D point (blue squares).”
The EuRoC micro aerial vehicle datasets (2016, methodology): “The visual-inertial sensor unit was calibrated with Kalibr5 (Furgale et al., 2013) prior to dataset collection.”
Extending kalibr: Calibrating the extrinsics of multiple IMUs and of individual axes (2016, methodology): “We suspect one of the reasons for this to lie in the fact that angular accelerations are required to model accelerations perceived in any location outside the accelerometer input axes (IA)—a quantity that is often not measured directly.1 While it would be possible to derive an estimate of angular ac”
Keyframe-based visual–inertial odometry using nonlinear optimization (2015, methodology): “An intrinsics and extrinsics calibration (distortion coefficients and TSCi ) was obtained using the method described by Furgale et al. (2013).”
Kimera: From SLAM to spatial perception with 3D dynamic scene graphs (2021, methodology): “We also calibrate the extrinsincs and intrinsics of the camera and IMU using Kalibr (Furgale et al. 2013).”
Visual-Inertial Navigation: A Concise Review (2019, methodology): “one of the state-of-the-art approaches, the Kalibr calibration toolbox [130, 134] uses a continuous-time basis function representation [137] of the sensor trajectory to calibrate both the extrinsics and intrinsics of a multi-sensor system in a batch fashion.”
The TUM VI Benchmark for Evaluating Visual-Inertial Odometry (2018, methodology): “2) Calibration Files: We also provide geometric calibration files which have been obtained from the processed calibration bags using the Kalibr toolbox2 [20].”