A GENERIC PROBABILISTIC MODEL AND A HIERARCHICAL SOLUTION FOR SENSOR LOCALIZATION IN NOISY AND RESTRICTED CONDITIONS
A generic probabilistic model, under fundamental Bayes’ rule and Markov assumption, is introduced to integrate the process of mobile platform localization with optical sensors. And based on it, three relative independent solutions, bundle adjustment, Kalman filtering and particle filtering are deduc...
Main Authors: | S. Ji, X. Yuan |
---|---|
Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2016-06-01
|
Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B1/193/2016/isprs-archives-XLI-B1-193-2016.pdf |
Similar Items
-
Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback
by: A. Emin Orhan, et al.
Published: (2017-07-01) -
Analysis on Noisy Boltzmann Machines and Noisy Restricted Boltzmann Machines
by: Wenhao Lu, et al.
Published: (2021-01-01) -
Probabilistic-Input, Noisy Conjunctive Models for Cognitive Diagnosis
by: Peida Zhan, et al.
Published: (2018-06-01) -
Probabilistic inference on noisy time series (PINTS)
by: Clerx, M, et al.
Published: (2019) -
Probabilistic Inference on Noisy Time Series (PINTS)
by: Michael Clerx, et al.
Published: (2019-07-01)