4 research outputs found
Adaptive Approximate Bayesian Computational Particle Filters for Underwater Terrain Aided Navigation
International audienceTo perform long-term and long-range missions, underwater vehicles need reliable navigation algorithms. This paper considers multi-beam Terrain Aided Navigation which can provide a drift-free navigation tool. This leads to an estimation problem with implicit observation equation and unknown likelihood. Indeed, the measurement sensor is considered to be a numerical black box model that introduces some unknown stochastic noise. We introduce a measurement updating procedure based on an adaptive kernel derived from Approximate Bayesian Computational filters. The proposed method is based on two well-known particle filters: Regularized Particle Filter and Rao-Blackwellized Particle Filter. Numerical results are presented and the robustness is demonstrated with respect to the original filters, yielding to twice as less non-convergence cases. The proposed method increases the robustness of particle-like filters while remaining computationally efficient
Adaptive Approximate Bayesian Computational Particle Filters for Underwater Terrain Aided Navigation
International audienceTo perform long-term and long-range missions, underwater vehicles need reliable navigation algorithms. This paper considers multi-beam Terrain Aided Navigation which can provide a drift-free navigation tool. This leads to an estimation problem with implicit observation equation and unknown likelihood. Indeed, the measurement sensor is considered to be a numerical black box model that introduces some unknown stochastic noise. We introduce a measurement updating procedure based on an adaptive kernel derived from Approximate Bayesian Computational filters. The proposed method is based on two well-known particle filters: Regularized Particle Filter and Rao-Blackwellized Particle Filter. Numerical results are presented and the robustness is demonstrated with respect to the original filters, yielding to twice as less non-convergence cases. The proposed method increases the robustness of particle-like filters while remaining computationally efficient
End-To-End Semi-supervised Learning for Differentiable Particle Filters
Recent advances in incorporating neural networks into particle filters
provide the desired flexibility to apply particle filters in large-scale
real-world applications. The dynamic and measurement models in this framework
are learnable through the differentiable implementation of particle filters.
Past efforts in optimising such models often require the knowledge of true
states which can be expensive to obtain or even unavailable in practice. In
this paper, in order to reduce the demand for annotated data, we present an
end-to-end learning objective based upon the maximisation of a
pseudo-likelihood function which can improve the estimation of states when
large portion of true states are unknown. We assess performance of the proposed
method in state estimation tasks in robotics with simulated and real-world
datasets.Comment: Accepted in ICRA 202
Adaptive Approximate Bayesian Computational Particle Filters for Underwater Terrain Aided Navigation
To perform long-term and long-range missions, underwater vehicles need reliable navigation algorithms. This paper considers multi-beam Terrain Aided Navigation which can provide a drift-free navigation tool. This leads to an estimation problem with implicit observation equation and unknown likelihood. Indeed, the measurement sensor is considered to be a numerical black box model that introduces some unknown stochastic noise. We introduce a measurement updating procedure based on an adaptive kernel derived from Approximate Bayesian Computational filters. The proposed method is based on two well-known particle filters: Regularized Particle Filter and Rao-Blackwellized Particle Filter. Numerical results are presented and the robustness is demonstrated with respect to the original filters, yielding to twice as less non-convergence cases. The proposed method increases the robustness of particle-like filters while remaining computationally efficient