2024-03-28T14:32:06Z
https://nagoya.repo.nii.ac.jp/oai
oai:nagoya.repo.nii.ac.jp:00027045
2023-01-16T04:29:09Z
1939:1940:1941
Simultaneous pose and reliability estimation using convolutional neural network and Rao–Blackwellized particle filter
Akai, Naoki
Morales, Luis Yoichi
Murase, Hiroshi
open access
This is an Accepted Manuscript of an article published by Taylor & Francis Group in Advanced Robotics on 27/08/2018, available online: http://www.tandfonline.com/10.1080/01691864.2018.
Localization
failure detection
reliability
convolutional neural network
Rao–Blackwellized particle filter
In this study, we propose a novel localization approach that simultaneously estimates the reliability of estimation results. In the approach, a convolutional neural network (CNN) is used to make decision whether the localization process has failed or not. We train the CNN using a dataset that includes successful localization results and faults. However, the decision will contain some noise and many misdetection results may occur when the decision made by the CNN is used directly to detect faults. Therefore, we estimate both a robot's pose and reliability of the localization results based on the decision. To simultaneously estimate the robot's pose and reliability, we propose a new graphical model and implement a Rao–Blackwellized particle filter based on the model. We evaluated the proposed approach based on simulations and actual environments, which showed that the reliability estimated by the proposed approach can be used as an exact criterion for detecting localization faults. In addition, we show that the proposed approach can be applied in actual environments even when a dataset created from a simulation is used to train the CNN.
ファイル公開:2019/08/27
Taylor & Francis
2018-08-27
eng
journal article
AM
http://hdl.handle.net/2237/00029245
https://nagoya.repo.nii.ac.jp/records/27045
https://doi.org/10.1080/01691864.2018.1509726
0169-1864
1568-5535
Advanced Robotics
32
17
930
944
https://nagoya.repo.nii.ac.jp/record/27045/files/paper.pdf
application/pdf
1.2 MB
2019-08-27