@inproceedings{c65235911eda44a9aa95eec2b144d688,
title = "Introducing PIVOT: Predictive Incremental Variable Ordering Tactic for Efficient Belief Space Planning",
abstract = "Belief Space Planning (BSP) is a fundamental technique in artificial intelligence and robotics, which is widely used in the solution of problems such as online autonomous navigation and manipulation. Unfortunately, BSP is computationally demanding, especially when dealing with high-dimensional state spaces. We thus introduce PIVOT: Predictive Incremental Variable Ordering Tactic, a novel approach to improve planning efficiency. Although variable ordering has been extensively used for the state inference problem, variable ordering specifically for planning has hardly been considered. Interestingly, this tactic can also lead to improved loop-closing efficiency during state inference. We use the approach in an active-SLAM scenario, and demonstrate a significant improvement in efficiency. This approach follows our previous work regarding efficient BSP via belief sparsification.",
author = "Khen Elimelech and Vadim Indelman",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 17th International Symposium of Robotics Research, ISRR 2019 ; Conference date: 06-10-2019 Through 10-10-2019",
year = "2022",
doi = "10.1007/978-3-030-95459-8\_6",
language = "الإنجليزيّة",
isbn = "9783030954581",
series = "Springer Proceedings in Advanced Robotics",
publisher = "Springer Nature",
pages = "85--101",
editor = "Tamim Asfour and Eiichi Yoshida and Jaeheung Park and Henrik Christensen and Oussama Khatib",
booktitle = "Robotics Research - The 19th International Symposium ISRR",
address = "الولايات المتّحدة",
}