Demo Data from VLDB 2026 / reviewers in the wild / expert
Yuya Mimasu
dblp:143/0268
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
3D vision · 77% Robot navigation and mapping · 23% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision › structure from motion
shape and motion estimation |
0.2 | 1 | 2015 | Simultaneous estimation of shape and motion of an asteroid for automatic navigation · ICRA 2015 |
Robotics › Robot navigation and mapping
mobile robot navigation |
0.1 | 1 | 2015 | Simultaneous estimation of shape and motion of an asteroid for automatic navigation · ICRA 2015 |
Methods — techniques the papers use, named apart from their topics
nonlinear optimization · 0.2expectation conditional maximization · 0.2auxiliary particle filter · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | Simultaneous estimation of shape and motion of an asteroid for automatic navigationabstractIn an asteroid exploration and sample return mission, accurate estimation of the shape and motion of the target asteroid is essential for selecting a touchdown site and navigating a spacecraft during touchdown operation. In this work, we present an automatic estimation method for the shape and motion of an asteroid, which is planned to be tested in future exploration missions including Japanese Hayabusa-2 [1]. Our task is to estimate the shape and rotation axis of the asteroid, as well as positions of the spacecraft from optical images. The proposed method is based on the expectation conditional-maximization (ECM) framework that consists of an auxiliary particle filter and nonlinear optimization techniques. One of our technical contributions is the estimation of the direction of rotation axis of the asteroid from monocular camera images, which are taken by the moving spacecraft. We conducted two experiments with synthetic data and an asteroid mock-up to show the validity of the proposed method and to present the numerical accuracy. Naoya Takeishi, Takehisa Yairi, Yuichi Tsuda, Fuyuto Terui, Naoko Ogawa, Yuya Mimasu |
ICRA | 6 |