Invariant Cubature Kalman Filtering-Based Visual-Inertial Odometry for Robot Pose Estimation

Sang, Xiaoyue; Li, Jingchao; Yuan, Zhaohui; Yu, Xiaojun; Zhang, Jingqin; Zhang, Jianrui; Yang, Pengfei

doi:10.1109/jsen.2022.3214293

Cited by 10 publications

(5 citation statements)

References 19 publications

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“…The robot's translational velocity is ascertained through the utilization of wheeled encoders, and the rotational velocity is determined by the servo mechanism employed in the Ackermann steering system. In the context of a mobile robot traversing a planar environment, the determination of its global position is facilitated by the consideration of its translational velocity and rotational angle, as shown in equation (1). Conforming to the inherent constraints of wheeled robots, it is commonly observed that the lateral and vertical velocities, denoted as b v y and b v z respectively, remain constrained to zero within the body frame…”

Section: Kinematic Model Of Wheeled Robotmentioning

confidence: 99%

“…State estimation plays a pivotal and indispensable role in the field of mobile robotics, encompassing domains such as autonomous driving, unmanned inspection, and indoor service [1]. By serving as the fundamental pillar of self-motion for mobile robots, state estimation aims to address the fundamental inquiry of 'Where am I' by leveraging a diverse range techniques often struggle to provide consistent and precise results.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Advancing robust state estimation of wheeled robots in degenerate environments: harnessing ground manifold and motion states

Liang,

Tao,

Zhu

et al. 2024

Meas. Sci. Technol.

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State estimation is crucial for enabling autonomous mobility in mobile robots. However, traditional localization methods often falter in degraded environments, including issues like visual occlusion, lidar performance degradation, and Global Navigation Satellite System(GNSS) signal interference. This paper presents a novel estimation approach for wheeled robots, exclusively utilizing proprioceptive sensors such as encoders and inertial measurement units (IMU). Initially, the motion manifolds extracted from the historical trajectories are used to assist the encoder in realizing the orientation estimation. Furthermore, a hybrid neural network is designed to categorize the robot's operational state, and the corresponding pseudo-constraints are added to improve the estimation accuracy. We utilize an error state Kalman filter for the encoder and IMU data fusion. Lastly, comprehensive testing is conducted using both datasets and real-world robotic platforms. The findings underscore that the integration of manifold and motion constraints within our proposed state estimator substantially elevates accuracy compared to conventional approaches. Compare with the methods commonly used in engineering, the accuracy of this method is improved by more than 20%. Crucially, this methodology enables dependable estimation even in degraded environments.

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Section: Kinematic Model Of Wheeled Robotmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Advancing robust state estimation of wheeled robots in degenerate environments: harnessing ground manifold and motion states

Liang,

Tao,

Zhu

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Some studies aim to improve the fusion robustness against non-linear natures from high system dynamics and complex environments, variants of Kalman filter such as the Extended Kalman Filter (EKF) [14][15][16][17], Multi-state Constraint Kalman Filter (MSCKF) [18][19][20][21][22], Unscented Kalman Filter (UKF) [23], Cubature Kalman filter (CKF) [24,25], and Particle Kalman Filter (PF) [26], have been proposed and evaluated. One challenge in the EKF-based VIO navigation system is handling significant non-linearity during brightness variations [15,17] and dynamic motion [15], which will cause feature-matching errors and degrade the overall performance.…”

Section: Related Work 21 Kalman Filter For Viomentioning

confidence: 99%

“…Another approach is proposed to handle additive noise: VIO uses camera observation to update the filter, allowing for avoiding over-parameterization and helping to reduce growth caused by UKF [23]. In most studies like [24,25], researchers have not focused on evaluating their proposed algorithms among complex scenarios such as light variation, rapid motion, motion blur, overexposures, and field of view, which deteriorate the accuracy and robustness of the state estimation.…”

Section: Related Work 21 Kalman Filter For Viomentioning

confidence: 99%

Integrating GRU with a Kalman Filter to Enhance Visual Inertial Odometry Performance in Complex Environments

Tabassum,

Xu,

Petrunin

et al. 2023

Aerospace

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To enhance system reliability and mitigate the vulnerabilities of the Global Navigation Satellite Systems (GNSS), it is common to fuse the Inertial Measurement Unit (IMU) and visual sensors with the GNSS receiver in the navigation system design, effectively enabling compensations with absolute positions and reducing data gaps. To address the shortcomings of a traditional Kalman Filter (KF), such as sensor errors, an imperfect non-linear system model, and KF estimation errors, a GRU-aided ESKF architecture is proposed to enhance the positioning performance. This study conducts Failure Mode and Effect Analysis (FMEA) to prioritize and identify the potential faults in the urban environment, facilitating the design of improved fault-tolerant system architecture. The identified primary fault events are data association errors and navigation environment errors during fault conditions of feature mismatch, especially in the presence of multiple failure modes. A hybrid federated navigation system architecture is employed using a Gated Recurrent Unit (GRU) to predict state increments for updating the state vector in the Error Estate Kalman Filter (ESKF) measurement step. The proposed algorithm’s performance is evaluated in a simulation environment in MATLAB under multiple visually degraded conditions. Comparative results provide evidence that the GRU-aided ESKF outperforms standard ESKF and state-of-the-art solutions like VINS-Mono, End-to-End VIO, and Self-Supervised VIO, exhibiting accuracy improvement in complex environments in terms of root mean square errors (RMSEs) and maximum errors.

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“…Hence, state estimation plays a pivotal role in unveiling the internal structure and dynamics of the system. It finds extensive applications in areas such as attitude determination, power system monitoring, vehicle dynamics, and target tracking [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Robust State Estimation Using the Maximum Correntropy Cubature Kalman Filter with Adaptive Cauchy-Kernel Size

Ye,

Lu,

Wang

et al. 2023

Electronics

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The maximum correntropy criterion (MCC), as an effective method for dealing with anomalous measurement noise, is widely applied in the design of filters. However, its performance largely depends on the proper setting of the kernel bandwidth, and currently, there is no efficient adaptive kernel adjustment mechanism. To deal with this issue, a new adaptive Cauchy-kernel maximum correntropy cubature Kalman filter (ACKMC-CKF) is proposed. This algorithm constructs adaptive factors for each dimension of the measurement system and establishes an entropy matrix with adaptive kernel sizes, enabling targeted handling of specific anomalies. Through simulation experiments in target tracking, the performance of the proposed algorithm was comprehensively validated. The results show that the ACKMC-CKF, through its flexible kernel adaptive mechanism, can effectively handle various types of anomalies. Not only does the algorithm demonstrate excellent reliability, but it also has low sensitivity to parameter settings, making it more broadly applicable in a variety of practical application scenarios.

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Invariant Cubature Kalman Filtering-Based Visual-Inertial Odometry for Robot Pose Estimation

Cited by 10 publications

References 19 publications

Advancing robust state estimation of wheeled robots in degenerate environments: harnessing ground manifold and motion states

Advancing robust state estimation of wheeled robots in degenerate environments: harnessing ground manifold and motion states

Integrating GRU with a Kalman Filter to Enhance Visual Inertial Odometry Performance in Complex Environments

Robust State Estimation Using the Maximum Correntropy Cubature Kalman Filter with Adaptive Cauchy-Kernel Size

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