Invariant Sliding Window Filtering for Attitude and Bias Estimation

Walsli, Alex; Arsenault, Jonathan; Forbes, James Richard

doi:10.23919/acc.2019.8814702

Cited by 3 publications

(5 citation statements)

References 11 publications

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“…For example, the covariance computation is straightforward in the IRTS smoother. In the forward pass, the covariance is computed using (9), and in the backward pass, the covariance is computed using (12). On the other hand, extracting the covariance associated with each state at each time step is cumbersome in IGN and MGN because a large, sparse, matrix must be inverted.…”

Section: Experimental Results Using the Starry Night Datasetmentioning

confidence: 99%

“…where z k = y k − yk and the corrected covariance is given by (9). Note that δχ ∧ 1 is an element of the Lie algebra associated with G.…”

Section: Multiplicative Rts Smoothingmentioning

confidence: 99%

“…Batch estimation in an invariant framework is considered in [7], with additional details and extensions given in [8], leading to an invariant Gauss-Newton (IGN) algorithm. Invariant sliding window filtering is also considered in [9] in the context of attitude and bias estimation. Applying the invariant framework to the simultaneous localization and mapping problem in a batch framework is considered in [5].…”

mentioning

confidence: 99%

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The Invariant Rauch-Tung-Striebel Smoother

Laan

Cohen

Arsenault

et al. 2020

IEEE Robot. Autom. Lett.

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This paper presents an invariant Rauch-Tung-Striebel (IRTS) smoother applicable to systems with states that are an element of a matrix Lie group. In particular, the extended Rauch-Tung-Striebel (RTS) smoother is adapted to work within a matrix Lie group framework. The main advantage of the invariant RTS (IRTS) smoother is that the linearization of the process and measurement models is independent of the state estimate resulting in state-estimate-independent Jacobians when certain technical requirements are met. A sample problem is considered that involves estimation of the three dimensional pose of a rigid body on SE(3), along with sensor biases. The multiplicative RTS (MRTS) smoother is also reviewed and is used as a direct comparison to the proposed IRTS smoother using experimental data. Both smoothing methods are also compared to invariant and multiplicative versions of the Gauss-Newton approach to solving the batch state estimation problem.

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Section: Experimental Results Using the Starry Night Datasetmentioning

confidence: 99%

“…where z k = y k − yk and the corrected covariance is given by (9). Note that δχ ∧ 1 is an element of the Lie algebra associated with G.…”

Section: Multiplicative Rts Smoothingmentioning

confidence: 99%

mentioning

confidence: 99%

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The Invariant Rauch-Tung-Striebel Smoother

Laan

Cohen

Arsenault

et al. 2020

IEEE Robot. Autom. Lett.

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“…The proposed framework was applied in an exemplary system of 2-D position estimation of mobile cars equipped with gyroscopes, velocity odometry, and GNSS measurements. On the other hand, Walsh et al [34] tested an IS for attitude and heading reference system with gyroscope bias in simulation. Also, Huai et al [35] applied Fig.…”

Section: A Group-affine Property and Invariant Smoothersmentioning

confidence: 99%

“…where Z i is the sensor observation at ith timestep, and L stands for the set of the long-term observations whereas a and b stand for the start timestep and the end timestep of the long-term observation, respectively. Similarly as (34), (43) is further transformed into a nonlinear least-square problem…”

Section: A State and Measurement Definitionmentioning

confidence: 99%

Invariant Smoother for Legged Robot State Estimation With Dynamic Contact Event Information

Yoon,

Kim,

Park

2024

IEEE Trans. Robot.

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This article proposes an invariant smoother for legged robot state estimation with the measurement of an inertial measurement unit and leg kinematics while assuming static foot contact. Because the proposed smoother is formulated with the residual functions with group-affine property, their Jacobians become independent from current state estimates. These state-independent Jacobians lead to better convergence properties in optimizing the cost in the smoother, especially under dynamic contact events. The proposed slip rejection method increases the uncertainty of static contact assumption when the robot has dynamic contact events. The estimated foot velocity, which is utilized to detect the dynamic contact events, is re-evaluated within the preserving time window. We also propose the contact loop method, a new measurement model asserting that foot position remains constant over multiple timesteps during stable contact. The proposed estimator is tested through online experiments, including indoor and 160 m-long outdoor experiments, and compared against state-of-the-art algorithms.

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