wawasan - Computer Vision - # Spatiotemporal Hand-Eye Calibration for Visual(-Inertial) Odometry Evaluation

Robust Spatiotemporal Hand-Eye Calibration for Accurate Trajectory Alignment in Visual(-Inertial) Odometry Evaluation

Q: How could the proposed algorithm be extended to handle time offset drift over long-duration trajectories?

To address time offset drift over long-duration trajectories, the proposed algorithm could incorporate a mechanism for continuous monitoring and adjustment of the time offset. This could involve periodically recalibrating the hand-eye relationship based on the evolving trajectory data. By implementing a feedback loop that continuously updates the time offset based on the changing conditions of the trajectories, the algorithm can adapt to any drift that may occur over time. Additionally, introducing a mechanism for detecting and correcting gradual changes in the time offset can help maintain the accuracy of the calibration over extended periods.

Q: What other types of sensor data, beyond VO/VIO, could be leveraged to further improve the accuracy and robustness of the hand-eye calibration?

In addition to Visual Odometry (VO) and Visual-Inertial Odometry (VIO) data, other types of sensor data that could be leveraged to enhance the accuracy and robustness of hand-eye calibration include: LIDAR Data: LIDAR sensors provide precise 3D point cloud information, which can be used in conjunction with visual data to improve the spatial alignment of trajectories during calibration. GPS Data: Global Positioning System (GPS) data can offer absolute positioning information, aiding in the global frame alignment of trajectories and reducing errors in the calibration process. IMU Data: Inertial Measurement Unit (IMU) data can provide additional motion information, such as acceleration and angular velocity, which can be used to refine the estimation of the hand-eye relationship. Magnetic Field Data: Magnetometer data can help in determining the orientation of sensors in relation to the Earth's magnetic field, contributing to the accurate alignment of frames during calibration. By integrating these diverse sensor modalities into the hand-eye calibration process, the algorithm can leverage a broader range of information to enhance accuracy and robustness.

Q: How could the hand-eye calibration process be integrated into the VO/VIO pipeline to enable online calibration and adaptation to changing environments?

To enable online calibration and adaptation to changing environments within the VO/VIO pipeline, the hand-eye calibration process can be integrated as follows: Continuous Monitoring: Implement a real-time monitoring system that tracks the performance of the hand-eye calibration during operation. This system can detect any deviations or drift in the calibration parameters and trigger recalibration when necessary. Dynamic Adjustment: Develop algorithms that can dynamically adjust the calibration parameters based on real-time sensor data. This adaptive approach allows the system to respond to changes in the environment or sensor characteristics promptly. Feedback Mechanism: Introduce a feedback loop that evaluates the accuracy of the calibration results based on the performance of the VO/VIO system. The feedback mechanism can provide insights into the effectiveness of the calibration and guide any necessary adjustments. Automatic Re-Calibration: Incorporate automated re-calibration routines that can be activated periodically or triggered by specific events. This ensures that the hand-eye calibration remains accurate and up-to-date in dynamic environments. By integrating these features into the VO/VIO pipeline, the hand-eye calibration process can adapt in real-time to changing conditions, ensuring optimal performance and accuracy in various environments.

Konsep Inti

A novel spatiotemporal hand-eye calibration algorithm that leverages multiple constraints from screw theory to achieve enhanced accuracy and robustness for aligning visual(-inertial) odometry trajectories with ground-truth data.

Abstrak

The content discusses a spatiotemporal hand-eye calibration algorithm designed for accurately aligning the estimated trajectory from visual(-inertial) odometry (VO/VIO) with a ground-truth trajectory obtained from a high-precision system like a motion capture (MoCap) system.

The key highlights are:

The algorithm addresses two main challenges in trajectory alignment: non-corresponding timestamps and different reference frames between the VO/VIO and ground-truth trajectories.
For time alignment, the algorithm improves the correlation analysis of the screw invariant to obtain synchronized trajectories with higher precision.
For spatial calibration, the algorithm constructs linear equations using local relative poses based on rotational constraint to fully utilize the motion information, rather than using global or inter-frame strategies.
A robust kernel based on screw theory is introduced to stabilize the linear solution, and a RANSAC framework is used to recover inlier data.
A nonlinear optimization tool is designed to jointly refine the time offset and the linear extrinsic solution.

The proposed algorithm demonstrates improved accuracy and robustness compared to state-of-the-art methods, especially when handling noisy and drifting VO/VIO trajectories. Experiments on public and simulated datasets, as well as the authors' own dataset collected using a VR headset and a MoCap system, validate the effectiveness of the algorithm.

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arxiv.org

Statistik

The average translation error is less than 0.02 m and the average rotation error is less than 0.75 degrees across the evaluated datasets.

Kutipan

"A common prerequisite for evaluating a visual(-inertial) odometry (VO/VIO) algorithm is to align the timestamps and the reference frame of its estimated trajectory with a reference ground-truth derived from a system of superior precision, such as a motion capture system."
"The spatiotemporal alignment problem above can be modeled as a classic hand-eye calibration problem: given the local frames of the ground-truth and estimated trajectory as the hand and the eye respectively, calculate the timestamp offset and estimate the homogeneous transformation between them."

Wawasan Utama Disaring Dari

A Spatiotemporal Hand-Eye Calibration for Trajectory Alignment in Visual(-Inertial) Odometry Evaluation

by Zichao Shu,L... pada arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.14894.pdf

A Spatiotemporal Hand-Eye Calibration for Trajectory Alignment in Visual(-Inertial) Odometry Evaluation

Pertanyaan yang Lebih Dalam

How could the proposed algorithm be extended to handle time offset drift over long-duration trajectories?

To address time offset drift over long-duration trajectories, the proposed algorithm could incorporate a mechanism for continuous monitoring and adjustment of the time offset. This could involve periodically recalibrating the hand-eye relationship based on the evolving trajectory data. By implementing a feedback loop that continuously updates the time offset based on the changing conditions of the trajectories, the algorithm can adapt to any drift that may occur over time. Additionally, introducing a mechanism for detecting and correcting gradual changes in the time offset can help maintain the accuracy of the calibration over extended periods.

What other types of sensor data, beyond VO/VIO, could be leveraged to further improve the accuracy and robustness of the hand-eye calibration?

In addition to Visual Odometry (VO) and Visual-Inertial Odometry (VIO) data, other types of sensor data that could be leveraged to enhance the accuracy and robustness of hand-eye calibration include:

LIDAR Data: LIDAR sensors provide precise 3D point cloud information, which can be used in conjunction with visual data to improve the spatial alignment of trajectories during calibration.

GPS Data: Global Positioning System (GPS) data can offer absolute positioning information, aiding in the global frame alignment of trajectories and reducing errors in the calibration process.

IMU Data: Inertial Measurement Unit (IMU) data can provide additional motion information, such as acceleration and angular velocity, which can be used to refine the estimation of the hand-eye relationship.

Magnetic Field Data: Magnetometer data can help in determining the orientation of sensors in relation to the Earth's magnetic field, contributing to the accurate alignment of frames during calibration.

By integrating these diverse sensor modalities into the hand-eye calibration process, the algorithm can leverage a broader range of information to enhance accuracy and robustness.

How could the hand-eye calibration process be integrated into the VO/VIO pipeline to enable online calibration and adaptation to changing environments?

To enable online calibration and adaptation to changing environments within the VO/VIO pipeline, the hand-eye calibration process can be integrated as follows:

Continuous Monitoring: Implement a real-time monitoring system that tracks the performance of the hand-eye calibration during operation. This system can detect any deviations or drift in the calibration parameters and trigger recalibration when necessary.

Dynamic Adjustment: Develop algorithms that can dynamically adjust the calibration parameters based on real-time sensor data. This adaptive approach allows the system to respond to changes in the environment or sensor characteristics promptly.

Feedback Mechanism: Introduce a feedback loop that evaluates the accuracy of the calibration results based on the performance of the VO/VIO system. The feedback mechanism can provide insights into the effectiveness of the calibration and guide any necessary adjustments.

Automatic Re-Calibration: Incorporate automated re-calibration routines that can be activated periodically or triggered by specific events. This ensures that the hand-eye calibration remains accurate and up-to-date in dynamic environments.

By integrating these features into the VO/VIO pipeline, the hand-eye calibration process can adapt in real-time to changing conditions, ensuring optimal performance and accuracy in various environments.