@article{ref1,
title="Reliability estimation for self-vehicle pose recognition result using LiDAR",
journal="Transactions of Society of Automotive Engineers of Japan",
year="2019",
author="Akai, Naoki and Morales, Luis Yoichi and Hirayama, Takatsugu and Murase, Hiroshi",
volume="50",
number="2",
pages="609-615",
abstract="This paper presents a reliability estimation method of localization results. In the method, an egovehicle pose and reliability are treated as hidden variables and are estimated simultaneously via Rao- Blackwellized particle filter (RBPF). The ego-vehicle pose is estimated by a sampling-based method, i.e., particle filter, and the reliability is estimated by an analytical method using prediction results of convolutional neural network (CNN). The CNN learns whether localization has failed or not and its output is used as an observable variable to estimate the reliability in the RBPF. Through experiments, it is shown that the estimated reliability could be used as an exact criterion for describing successful and fault localization results.<p /><p>Language: ja</p>",
language="ja",
issn="0287-8321",
doi="10.11351/jsaeronbun.50.609",
url="http://dx.doi.org/10.11351/jsaeronbun.50.609"
}