Degeneration-Aware Localization with Arbitrary Global-Local Sensor Fusion

Ding, Xiaqing; Han, Fuzhang; Yang, Tianfeng; Wang, Yue; Xiong, Rong

doi:10.3390/s21124042

Cited by 10 publications

(3 citation statements)

References 34 publications

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“…Theoretically, the eigenvalues corresponding to the degeneration dimensions are precisely zero while are typically a small value in the actual calculation due to the noise of data and limited computational accuracy. Hence, we first calculate the eigenvalues {λ i } of the Hessian matrix H L H L and refer to the heuristic method in [20] to determine how well the geometry feature of the scene. If the eigenvalue is smaller than the thread, we can infer the existence of degeneration.…”

Section: B Imu Propagationmentioning

confidence: 99%

DAMS-LIO: A Degeneration-Aware and Modular Sensor-Fusion LiDAR-inertial Odometry

Fuzhang~Han¹,

Han~Zheng²,

Wenjun~Huang³

et al. 2023

Preprint

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The fusion scheme is crucial to the multi-sensor fusion method that is the promising solution to the state estimation in complex and extreme environments like underground mines and planetary surfaces. In this work, a light-weight iEKF-based LiDAR-inertial odometry system is presented, which utilizes a degeneration-aware and modular sensor-fusion pipeline that takes both LiDAR points and relative pose from another odometry as the measurement in the update process only when degeneration is detected. Both the CRLB theory and simulation test are used to demonstrate the higher accuracy of our method compared to methods using a single observation. Furthermore, the proposed system is evaluated in perceptually challenging datasets against various state-of-the-art sensor-fusion methods. The results show that the proposed system achieves realtime and high estimation accuracy performance despite the challenging environment and poor observations.

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Section: B Imu Propagationmentioning

confidence: 99%

DAMS-LIO: A Degeneration-Aware and Modular Sensor-Fusion LiDAR-inertial Odometry

Fuzhang~Han¹,

Han~Zheng²,

Wenjun~Huang³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In [29], a tightly-coupled approach is proposed to optimize all measurements together with respect to all states. When both odometry and map-based measurements are integrated, a degeneracy analysis is performed in [19]. In general, optimization-based VIL methods have superior accuracy than filtering-based methods because the nonlinear system can be re-linearized with a cost of more expensive resources.…”

Section: B Drift-free Visual-inertial Localizationmentioning

confidence: 99%

Toward Consistent and Efficient Map-based Visual-inertial Localization: Theory Framework and Filter Design

Zhang¹,

Yang²,

Huang³

et al. 2022

Preprint

Self Cite

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This paper focuses on designing a consistent and efficient filter for map-based visual-inertial localization. First, we propose a new Lie group with its algebra, based on which a novel invariant extended Kalman filter (invariant EKF) is designed. We theoretically prove that, when we do not consider the uncertainty of the map information, the proposed invariant EKF can naturally maintain the correct observability properties of the system. To consider the uncertainty of the map information, we introduce a Schmidt filter. With the Schmidt filter, the uncertainty of the map information can be taken into consideration to avoid over-confident estimation while the computation cost only increases linearly with the size of the map keyframes. In addition, we introduce an easily implemented observability-constrained technique because directly combining the invariant EKF with the Schmidt filter cannot maintain the correct observability properties of the system that considers the uncertainty of the map information. Finally, we validate our proposed system's high consistency, accuracy, and efficiency via extensive simulations and real-world experiments.

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“…In [24], a tightly coupled approach is proposed to optimize all measurements together with respect to all states. When both odometry and map-based measurements are integrated, a degeneracy analysis is performed in [17]. In general, the optimizationbased visual-inertial localization has superior accuracy than the filtering-based methods because the nonlinear system can be re-linearized with the cost of more expensive resources.…”

Section: B Optimization Based Visual-inertial Localizationmentioning

confidence: 99%

Map-based Visual-Inertial Localization: Consistency and Complexity

Zhang¹,

Jiao²,

Huang³

et al. 2022

Preprint

Self Cite

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Drift-free localization is essential for autonomous vehicles. In this paper, we address the problem by proposing a filter-based framework, which integrates the visual-inertial odometry and the measurements of the features in the prebuilt map. In this framework, the transformation between the odometry frame and the map frame is augmented into the state and estimated on the fly. Besides, we maintain only the keyframe poses in the map and employ Schmidt extended Kalman filter to update the state partially, so that the uncertainty of the map information can be consistently considered with low computational cost. Moreover, we theoretically demonstrate that the ever-changing linearization points of the estimated state can introduce spurious information to the augmented system and make the original four-dimensional unobservable subspace vanish, leading to inconsistent estimation in practice.To relieve this problem, we employ first-estimate Jacobian (FEJ) to maintain the correct observability properties of the augmented system. Furthermore, we introduce an observability-constrained updating method to compensate for the significant accumulated error after the long-term absence (can be 3 minutes and 1 km) of map-based measurements. Through simulations, the consistent estimation of our proposed algorithm is validated. Through realworld experiments, we demonstrate that our proposed algorithm runs successfully on four kinds of datasets with the lower computational cost (20% time-saving) and the better estimation accuracy (45% trajectory error reduction) compared with the baseline algorithm VINS-Fusion, whereas VINS-Fusion fails to give bounded localization performance on three of four datasets because of its inconsistent estimation.

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Degeneration-Aware Localization with Arbitrary Global-Local Sensor Fusion

Cited by 10 publications

References 34 publications

DAMS-LIO: A Degeneration-Aware and Modular Sensor-Fusion LiDAR-inertial Odometry

DAMS-LIO: A Degeneration-Aware and Modular Sensor-Fusion LiDAR-inertial Odometry

Toward Consistent and Efficient Map-based Visual-inertial Localization: Theory Framework and Filter Design

Map-based Visual-Inertial Localization: Consistency and Complexity

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