Dynamic Sensor Matching based on Geomagnetic Inertial Navigation

@inproceedings{Muller2022DynamicSM,
  title={Dynamic Sensor Matching based on Geomagnetic Inertial Navigation},
  author={S. Muller and Dieter Kranzlmuller},
  year={2022}
}
Optical sensors can capture dynamic environments and derive depth information in near real-time. The quality of these digital reconstructions is determined by factors like illumination, surface and texture conditions, sensing speed and other sensor characteristics as well as the sensor-object relations. Improvements can be obtained by using dynamically collected data from multiple sensors. However, matching the data from multiple sensors requires a shared world coordinate system. We present a… 

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References

SHOWING 1-10 OF 21 REFERENCES

Cameras and Inertial/Magnetic Sensor Units Alignment Calibration

  • Zhiqiang Zhang
  • Physics
    IEEE Transactions on Instrumentation and Measurement
  • 2016
A two-step iterative algorithm is proposed to solve the rotational alignment calibration problem by exploiting the intrinsic restrictions among the coordinate transformations, formulated by a simplified hand-eye equation AX = X B (A, X, and B are all rotation matrices).

Infrastructureless indoor navigation with an hybrid magneto-inertial and depth sensor system

This work proposes a simple fusion strategy using the magneto-inertial tachymeter in the predition step and the depth image registration in the correction step, and demonstrates accurate reconstruction of indoor trajectories in infrastructureless environments, with a drift often below one percent of the length of the trajectory.

Dynamic Sensor Matching for Parallel Point Cloud Data Acquisition

This work builds the foundation for dynamic and real-time based generation of digital twins with the aid of realsensor data by proposing the simultaneous use of multiple and dynamically arranged cameras.

A new approach to vision-aided inertial navigation

A visual odometry system with an aided inertial navigation filter is combined to produce a precise and robust navigation system that does not rely on external infrastructure and to handle uncertainties in the system in a principled manner.

Inertial navigation aid indoor navigation based on the establishment of accurate magnetic reference map

By using the Kriging method, the high precision magnetic reference map is established in the server to improve the positioning accuracy, and the inertial navigation technology is used to shorten the matching time.

Indoor location sensing using geo-magnetism

An indoor positioning system that measures location using disturbances of the Earth's magnetic field caused by structural steel elements in a building that demonstrates accuracy within 1 meter 88% of the time in experiments in two buildings and across multiple floors within the buildings.

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

An end-to-end review of the hardware and software methods required for sensor fusion object detection and some of the challenges in the sensor fusion field are highlighted and possible future research directions for automated driving systems are proposed.

Asynchronous Stereo Vision for Event-Driven Dynamic Stereo Sensor Using an Adaptive Cooperative Approach

An adaptive cooperative approach towards the 3D reconstruction tailored for a bio-inspired depth camera: the stereo dynamic vision sensor, in which the history of the recent activity in the scene is stored to serve as spatiotemporal context for each incoming event.

Visual-Inertial Navigation: A Concise Review

  • G. Huang
  • Computer Science
    2019 International Conference on Robotics and Automation (ICRA)
  • 2019
This paper surveys thoroughly the research efforts taken in visual-inertial navigation research and strives to provide a concise but complete review of the related work in the hope to accelerate the VINS research and beyond in the authors' society as a whole.

Triangulation-Based Approaches to Three-Dimensional Scene Reconstruction

An introduction to the field of camera calibration is given, and an overview of the variety of existing methods for establishing point correspondences is provided, including classical and also new feature-based, correlation- based, dense, and spatiotemporal approaches.