VLDB 2026 Research / reviewers in the wild / expert
Chin-Yuan Tseng
dblp:128/0584
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2013
—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 |
Robot navigation and mapping · 100% | |
| Human-computer interaction and pervasive computing
1 paper |
Wearable and physiological sensing · 100% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot navigation and mapping
localization |
0.2 | 1 | 2013 | IMU-assisted monocular visual odometry including the human walking model for wearable applications · ICRA 2013 |
Robotics › Robot navigation and mapping › visual odometry
monocular visual odometry |
0.2 | 1 | 2013 | IMU-assisted monocular visual odometry including the human walking model for wearable applications · ICRA 2013 |
Robotics › Robot navigation and mapping › sensor fusion
visual-inertial fusion |
0.2 | 1 | 2013 | IMU-assisted monocular visual odometry including the human walking model for wearable applications · ICRA 2013 |
Robotics › Robot navigation and mapping
visual odometry |
0.2 | 1 | 2013 | IMU-assisted monocular visual odometry including the human walking model for wearable applications · ICRA 2013 |
Methods — techniques the papers use, named apart from their topics
unscented kalman filter · 0.3kinematic walking model · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2013 | IMU-assisted monocular visual odometry including the human walking model for wearable applicationsabstractIn this paper, we present a novel approach for monocular visual odometry combined with an inertial measurement unit to estimate the human walking trajectory. The scale factor is computed from the walking speed estimation derived by the kinematic model of human walking. This model not only keeps the important characteristic in biped rolling-foot model, but also makes the speed estimation feasible using human body acceleration. A wearable and calibrated IMU-Camera device is made to mount on the center of human's waist for experimental verification. With the human walking speed and gyroscope measurement, an Unscented Kalman filter is implemented to estimate the current scale factor and refine the attitude of the camera. Experiments in an indoor environment are conducted to show the effectiveness of the proposed method. Jwu-Sheng Hu, Chin-Yuan Tseng, Ming-yuan Chen, Kuan-Chun Sun |
ICRA | 2 |