Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 193-198, 2016
https://doi.org/10.5194/isprs-archives-XLI-B1-193-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
03 Jun 2016
A GENERIC PROBABILISTIC MODEL AND A HIERARCHICAL SOLUTION FOR SENSOR LOCALIZATION IN NOISY AND RESTRICTED CONDITIONS
S. Ji1,2 and X. Yuan1 1School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430049, China
2RSISE, Australian National University (ANU), Canberra ACT 2600, Australia
Keywords: Generic Probabilistic Localization Model, Bundle Adjustment, Kalman Filtering, Particle Filtering Abstract. 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 deduced under different and additional restrictions. We want to prove that first, Kalman filtering, may be a better initial-value supplier for bundle adjustment than traditional relative orientation in irregular strips and networks or failed tie-point extraction. Second, in high noisy conditions, particle filtering can act as a bridge for gap binding when a large number of gross errors fail a Kalman filtering or a bundle adjustment. Third, both filtering methods, which help reduce the error propagation and eliminate gross errors, guarantee a global and static bundle adjustment, who requires the strictest initial values and control conditions. The main innovation is about the integrated processing of stochastic errors and gross errors in sensor observations, and the integration of the three most used solutions, bundle adjustment, Kalman filtering and particle filtering into a generic probabilistic localization model. The tests in noisy and restricted situations are designed and examined to prove them.
Conference paper (PDF, 1023 KB)


Citation: Ji, S. and Yuan, X.: A GENERIC PROBABILISTIC MODEL AND A HIERARCHICAL SOLUTION FOR SENSOR LOCALIZATION IN NOISY AND RESTRICTED CONDITIONS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 193-198, https://doi.org/10.5194/isprs-archives-XLI-B1-193-2016, 2016.

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