Streaming Monte Carlo Pose Estimation for Autonomous Object Modeling

This work contributes the optimization of a streaming pose estimation particle filter and its integration into an autonomous object modeling approach. The particle filter is advanced by an additional pose optimization in the particle weighting step. By integrating the method into the autonomous obje...

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Bibliographic Details
Published in2016 13th Conference on Computer and Robot Vision (CRV) pp. 156 - 163
Main Authors Rink, Christian, Kriegel, Simon
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2016
Subjects
3D modeling
Active sensing
Laser modes
Laser scanning
Planning
Pose estimation
Service robots
Solid modeling
Three-dimensional displays
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Summary:This work contributes the optimization of a streaming pose estimation particle filter and its integration into an autonomous object modeling approach. The particle filter is advanced by an additional pose optimization in the particle weighting step. By integrating the method into the autonomous object modeling approach, the repositioning of objects is enabled, which is often necessary in order to acquire complete models. Experiments show that the usage of iterative closest point is too restrictive for general transformations. The used Monte Carlo method enables a robust pose estimation without loss of time and with high precision. Further, it is shown that the overall modeling results are improved clearly.
DOI:10.1109/CRV.2016.21