Zobrazeno 1 - 10
of 454
pro vyhledávání: '"A, Bonnabel"'
Publikováno v:
2024 IEEE 63th Annual Conference on Decision and Control (CDC), Dec 2024, Milan, Italy
We consider the problem of observer design for a nonholonomic car (more generally a wheeled robot) equipped with wheel speeds with unknown wheel radius, and whose position is measured via a GNSS antenna placed at an unknown position in the car. In a
Externí odkaz:
http://arxiv.org/abs/2409.07050
We consider the problem of computing tractable approximations of time-dependent d x d large positive semi-definite (PSD) matrices defined as solutions of a matrix differential equation. We propose to use "low-rank plus diagonal" PSD matrices as appro
Externí odkaz:
http://arxiv.org/abs/2407.03373
Publikováno v:
2024 Conference on Decision and Control, Dec 2024, Milano, Italy
We consider the problem of stochastic optimal control, where the state-feedback control policies take the form of a probability distribution and where a penalty on the entropy is added. By viewing the cost function as a Kullback- Leibler (KL) diverge
Externí odkaz:
http://arxiv.org/abs/2404.14806
Publikováno v:
62nd IEEE Conference on Decision and Control (CDC), Singapore, Singapore, 2023, pp. 8665-8671
In this paper, we focus on developing an Invariant Extended Kalman Filter (IEKF) for extended pose estimation for a noisy system with state equality constraints. We treat those constraints as noise-free pseudo-measurements. To this aim, we provide a
Externí odkaz:
http://arxiv.org/abs/2404.10687
In this paper, we introduce the Iterated Invariant Extended Kalman Filter (IIEKF), which is an invariant extended Kalman filter (IEKF) where the updated state in the light of the latest measurement is defined as a maximum a posteriori (MAP) estimate.
Externí odkaz:
http://arxiv.org/abs/2404.10665
To enhance accuracy of robot state estimation, active sensing (or perception-aware) methods seek trajectories that maximize the information gathered by the sensors. To this aim, one possibility is to seek trajectories that minimize the (estimation er
Externí odkaz:
http://arxiv.org/abs/2402.17569
In estimation theory, the Kushner equation provides the evolution of the probability density of the state of a dynamical system given continuous-time observations. Building upon our recent work, we propose a new way to approximate the solution of the
Externí odkaz:
http://arxiv.org/abs/2310.01859
Publikováno v:
2024 IEEE 63th Annual Conference on Decision and Control (CDC), Dec 2024, Milan, Italy
In this article we investigate smoothing (i.e., optimisation-based) estimation techniques for robot localization using an IMU aided by other localization sensors. We more particularly focus on Invariant Smoothing (IS), a variant based on the use of n
Externí odkaz:
http://arxiv.org/abs/2309.13903
In this paper we provide novel closed-form expressions enabling differentiation of any scalar function of the Kalman filter's outputs with respect to all its tuning parameters and to the measurements. The approach differs from the previous well-known
Externí odkaz:
http://arxiv.org/abs/2303.16846
We consider the problem of computing a Gaussian approximation to the posterior distribution of a parameter given a large number N of observations and a Gaussian prior, when the dimension of the parameter d is also large. To address this problem we bu
Externí odkaz:
http://arxiv.org/abs/2303.14195