Extending kalibr: Calibrating the extrinsics of multiple IMUs and of individual axes

By J. Rehder, Janosch Nikolic, Thomas Schneider, Timo Hinzmann, Roland Yves Siegwart. IEEE International Conference on Robotics and Automation, 2016

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The TUM VI Benchmark for Evaluating Visual-Inertial Odometry (2018, background): “• the relative pose between cameras and IMU, • the time shift between MoCap and IMU time, • the time shift between camera and IMU time, • the relative pose between the cameras, • the relative poses TMI and TWG, • coarse initial accelerometer and gyroscope biases ba and bg, • axis scaling and misalig”
Kimera: From SLAM to spatial perception with 3D dynamic scene graphs (2021, methodology): “Furthermore, the transform between the two RealSense devices is estimated using the Kalibr extension for IMU-to-IMU extrinsic calibration (Rehder et al., 2016).”
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.”