Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data.

Chin-Yuan Tseng

dblp:128/0584 · DBLP profile ↗
← Back
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

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping
localization
0.212013
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
0.212013
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
0.212013
IMU-assisted monocular visual odometry including the human walking model for wearable applications · ICRA 2013
Robotics › Robot navigation and mapping
visual odometry
0.212013
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
YearPublicationVenuePosition
2013 IMU-assisted monocular visual odometry including the human walking model for wearable applications
abstract
In 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
ICRA2