### Abstract

A state estimation scheme that uses a global representation of the configuration space for rigid body motion, is presented. This estimation scheme works as a deterministic filter which selects a set of sigma points obtained from the unscented transform based on exponential coordinates centered at the current state estimate. These sigma points and the state estimate are propagated in time along the dynamics until the next set of state measurements are obtained. This propagation (prediction) stage is carried out using a Lie group variational integrator, which discretizes the continuous equations of motion of the rigid body. The propagated sigma points and state estimate are enclosed by a minimum volume ellipsoid at the measurement instant. It is assumed that all states are measured at a constant measurement sample rate and that state measurement errors, expressed in the exponential coordinates, are bounded by an ellipsoidal bound. The update stage of the filter then consists of finding the minimum trace ellipsoid that contains the intersection of this measurement uncertainty bound and the minimum volume ellipsoid enclosing the propagated sigma points. This updated ellipsoid provides the filtered uncertainty bound and its center provides the updated state estimate. A new set of sigma points is selected from this ellipsoid and the propagation and update steps are repeated between measurement instants.

Original language | English (US) |
---|---|

Article number | 6426899 |

Pages (from-to) | 7498-7503 |

Number of pages | 6 |

Journal | Proceedings of the IEEE Conference on Decision and Control |

DOIs | |

State | Published - Dec 1 2012 |

Externally published | Yes |

Event | 51st IEEE Conference on Decision and Control, CDC 2012 - Maui, HI, United States Duration: Dec 10 2012 → Dec 13 2012 |

### Fingerprint

### ASJC Scopus subject areas

- Control and Systems Engineering
- Modeling and Simulation
- Control and Optimization

### Cite this

*Proceedings of the IEEE Conference on Decision and Control*, 7498-7503. [6426899]. https://doi.org/10.1109/CDC.2012.6426899

**Unscented state estimation for rigid body motion on SE(3).** / Bohn, Jan; Sanyal, Amit.

Research output: Contribution to journal › Conference article

}

TY - JOUR

T1 - Unscented state estimation for rigid body motion on SE(3)

AU - Bohn, Jan

AU - Sanyal, Amit

PY - 2012/12/1

Y1 - 2012/12/1

N2 - A state estimation scheme that uses a global representation of the configuration space for rigid body motion, is presented. This estimation scheme works as a deterministic filter which selects a set of sigma points obtained from the unscented transform based on exponential coordinates centered at the current state estimate. These sigma points and the state estimate are propagated in time along the dynamics until the next set of state measurements are obtained. This propagation (prediction) stage is carried out using a Lie group variational integrator, which discretizes the continuous equations of motion of the rigid body. The propagated sigma points and state estimate are enclosed by a minimum volume ellipsoid at the measurement instant. It is assumed that all states are measured at a constant measurement sample rate and that state measurement errors, expressed in the exponential coordinates, are bounded by an ellipsoidal bound. The update stage of the filter then consists of finding the minimum trace ellipsoid that contains the intersection of this measurement uncertainty bound and the minimum volume ellipsoid enclosing the propagated sigma points. This updated ellipsoid provides the filtered uncertainty bound and its center provides the updated state estimate. A new set of sigma points is selected from this ellipsoid and the propagation and update steps are repeated between measurement instants.

AB - A state estimation scheme that uses a global representation of the configuration space for rigid body motion, is presented. This estimation scheme works as a deterministic filter which selects a set of sigma points obtained from the unscented transform based on exponential coordinates centered at the current state estimate. These sigma points and the state estimate are propagated in time along the dynamics until the next set of state measurements are obtained. This propagation (prediction) stage is carried out using a Lie group variational integrator, which discretizes the continuous equations of motion of the rigid body. The propagated sigma points and state estimate are enclosed by a minimum volume ellipsoid at the measurement instant. It is assumed that all states are measured at a constant measurement sample rate and that state measurement errors, expressed in the exponential coordinates, are bounded by an ellipsoidal bound. The update stage of the filter then consists of finding the minimum trace ellipsoid that contains the intersection of this measurement uncertainty bound and the minimum volume ellipsoid enclosing the propagated sigma points. This updated ellipsoid provides the filtered uncertainty bound and its center provides the updated state estimate. A new set of sigma points is selected from this ellipsoid and the propagation and update steps are repeated between measurement instants.

UR - http://www.scopus.com/inward/record.url?scp=84874282896&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84874282896&partnerID=8YFLogxK

U2 - 10.1109/CDC.2012.6426899

DO - 10.1109/CDC.2012.6426899

M3 - Conference article

AN - SCOPUS:84874282896

SP - 7498

EP - 7503

JO - Proceedings of the IEEE Conference on Decision and Control

JF - Proceedings of the IEEE Conference on Decision and Control

SN - 0191-2216

M1 - 6426899

ER -