• Corpus ID: 238198353

Targetless Extrinsic Calibration of Stereo Cameras, Thermal Cameras, and Laser Sensors in the Wild

@article{Fu2021TargetlessEC,
  title={Targetless Extrinsic Calibration of Stereo Cameras, Thermal Cameras, and Laser Sensors in the Wild},
  author={Tai Fu and Huai Yu and Yaoyu Hu and Sebastian A. Scherer},
  journal={ArXiv},
  year={2021},
  volume={abs/2109.13414}
}
—The fusion of multi-modal sensors has become increasingly popular in autonomous driving and intelligent robots since it can provide richer information than any single sensor, enhance reliability in complex environments. Multi-sensor extrinsic calibration is one of the key factors of sensor fusion. However, such calibration is difficult due to the variety of sensor modalities and the requirement of calibration targets and human labor. In this paper, we demonstrate a new targetless cross-modal… 
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References

SHOWING 1-10 OF 20 REFERENCES

Automatic Online Calibration of Cameras and Lasers

Two new real-time techniques that enable camera-laser calibration online, automatically, and in arbitrary environments are introduced, including a probabilistic monitoring algorithm that can detect a sudden miscalibration in a fraction of a second, and a continuous calibration optimizer that adjusts transform offsets in real time, tracking gradual sensor drift as it occurs.

Automatic extrinsic calibration for lidar-stereo vehicle sensor setups

This work presents a method for extrinsic calibration of lidar-stereo camera pairs without user intervention, aimed to cope with the constraints commonly found in automotive setups, such as low-resolution and specific sensor poses.

LiDAR-Camera Calibration using 3D-3D Point correspondences

A novel pipeline and experimental setup is proposed to find accurate rigid-body transformation for extrinsically calibrating a LiDAR and a camera and it is demonstrated how two cameras with no overlapping field-of-view can also be calibrated extrinsic using 3D point correspondences.

Extrinsic self calibration of a camera and a 3D laser range finder from natural scenes

A new approach for the extrinsic calibration of a camera with a 3D laser range finder, that can be done on the fly and brings 3D computer vision systems out of the laboratory and into practical use.

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

A mutual information (MI) based algorithm for automatic extrinsic calibration of a 3D laser scanner and optical camera system is reported on and it is shown that the sample variance of the estimated parameters empirically approaches the CRLB for a sufficient number of views.

Data Fusion Calibration for a 3D Laser Range Finder and a Camera using Inertial Data

A new method to perform the extrinsic calibration between a pinhole camera and a 3D-LRF with the aid of an Inertial Measurement Unit (IMU) is proposed, which is innovate in terms of higher exibility and wider range of application.

Depth and thermal sensor fusion to enhance 3D thermographic reconstruction.

This paper presents a Thermal-guided Iterative Closest Point (T-ICP) methodology to facilitate reliable 3D thermal scanning applications and demonstrates that complimentary information captured by multimodal sensors can be utilized to improve performance of 3D thermographic reconstruction.

Line-Based 2-D–3-D Registration and Camera Localization in Structured Environments

This article proposes a new 2-D–3-D registration method to estimate 1-D-2-D line feature correspondences and the camera pose in untextured point clouds of structured environments and demonstrates the effectiveness on the synthetic and real data set with repeated structures and rapid depth changes.

A Method for Registration of 3-D Shapes

A general-purpose, representation-independent method for the accurate and computationally efficient registration of 3-D shapes including free-form curves and surfaces based on the iterative closest point (ICP) algorithm.

A Computational Approach to Edge Detection

  • J. Canny
  • Computer Science
    IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 1986
There is a natural uncertainty principle between detection and localization performance, which are the two main goals, and with this principle a single operator shape is derived which is optimal at any scale.