The precise calibration of all sensor modalities is crucial for multi-modal sensor fusion in robotics, which is used for 3D pose estimation (odometry). To achieve optimal calibration, deterministic filters or non-dete...
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ISBN:
(纸本)9783031667428;9783031667435
The precise calibration of all sensor modalities is crucial for multi-modal sensor fusion in robotics, which is used for 3D pose estimation (odometry). To achieve optimal calibration, deterministic filters or non-deterministic optimization models can be used to estimate time-invariant intrinsic and extrinsic parameters. Using a gps-aided optimizer bootstrapping algorithm, we introduce a novel optimization-based approach for intrinsic and extrinsic calibration of an RGB-D-IMU visual-inertial setup. Our front-end pipeline relies on an optical flow Visual Odometry (VO) method to obtain reliable initial estimates for the RGB camera intrinsics and trajectory. In addition to calibrating all time-invariant properties, our back-end optimizes spatio-temporal parameters such as the target's pose, 3D point cloud, and IMUbiases. The proposed complete RGB-D-IMU setup calibration algorithm is validated on both real-world and high-quality simulated sequences. We conducted ablation studies on ground and aerial vehicles to assess the contribution of each sensor in the multi-modal (RGB-D-IMU-gps) setup to the vehicle's pose estimation accuracy. Our GitHub repository contains the proposed algorithm implementation: https://***/AbanobSoliman/HCALIB
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