TY - GEN
T1 - Mobile Robot Localization Considering the Attributes of Objects to Prevent the Kidnapped Robot Problem
AU - Harada, Taiki
AU - Yorozu, Ayanori
AU - Takahashi, Masaki
N1 - Funding Information:
Acknowledgment. This study was supported by JSPS KAKENHI Grant Number 17K14619 and “A Framework PRINTEPS to Develop Practical Artificial Intelligence” of the Core Research for Evolutional Science and Technology (CREST) of the Japan Science and Technology Agency (JST) under Grant Number JPMJCR14E3.
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - To prevent the occurrence of the kidnapped robot problem, it is vital to evaluate the likelihood of each particle considering an environmental change. Moving objects are one of the leading causes of environmental change, and each object has its own movability. For example, a chair has high movability because it is designed to move and often interacts with humans. However, walls or shelves have low movability because they are designed not to move, and they interact less often with humans. Therefore, in this study, we define classes of objects and their movability. We propose a localization approach that focuses on the association between sensor information obtained from objects whose movability is low and prior map by considering classes and movability, to prevent the occurrence of the kidnapped robot problem.
AB - To prevent the occurrence of the kidnapped robot problem, it is vital to evaluate the likelihood of each particle considering an environmental change. Moving objects are one of the leading causes of environmental change, and each object has its own movability. For example, a chair has high movability because it is designed to move and often interacts with humans. However, walls or shelves have low movability because they are designed not to move, and they interact less often with humans. Therefore, in this study, we define classes of objects and their movability. We propose a localization approach that focuses on the association between sensor information obtained from objects whose movability is low and prior map by considering classes and movability, to prevent the occurrence of the kidnapped robot problem.
KW - Localization
KW - Mobile robot
KW - Semantic information
KW - Sensor fusion
UR - http://www.scopus.com/inward/record.url?scp=85128753651&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85128753651&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-95892-3_4
DO - 10.1007/978-3-030-95892-3_4
M3 - Conference contribution
AN - SCOPUS:85128753651
SN - 9783030958916
T3 - Lecture Notes in Networks and Systems
SP - 41
EP - 53
BT - Intelligent Autonomous Systems 16 - Proceedings of the 16th International Conference IAS-16
A2 - Ang Jr, Marcelo H.
A2 - Asama, Hajime
A2 - Lin, Wei
A2 - Foong, Shaohui
PB - Springer Science and Business Media Deutschland GmbH
T2 - 16th International Conference on Intelligent Autonomous Systems, IAS-16 2020
Y2 - 22 June 2021 through 25 June 2021
ER -