Name
Abstract | ||
|---|---|---|
Inertial information processing plays a pivotal role in ego-motion awareness for mobile agents, as inertial measurements are entirely egocentric and not environment dependent. However, they are affected greatly by changes in sensor placement/orientation or motion dynamics, and it is infeasible to collect labelled data from every domain. To overcome the challenges of domain adaptation on long sensory sequences, we propose a novel framework that extracts domain-invariant features of raw sequences from arbitrary domains, and transforms to new domains without any paired data. Through the experiments, we demonstrate that it is able to efficiently and effectively convert the raw sequence from a new unlabelled target domain into an accurate inertial trajectory, benefiting from the physical motion knowledge transferred from the labelled source domain. We also conduct real-world experiments to show our framework can reconstruct physically meaningful trajectories from raw IMU measurements obtained with a standard mobile phone in various attachments. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Learning | Inertial frame of reference,Computer vision,Inertial tracking,Information processing,Domain adaptation,Inertial measurement unit,Artificial intelligence,Mobile phone,Paired Data,Trajectory,Mathematics,Machine learning |
DocType | Volume | Citations |
Journal | abs/1810.02076 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Changhao Chen | 1 | 27 | 8.71 |
| Yishu Miao | 2 | 178 | 11.44 |
| Chris Xiaoxuan Lu | 3 | 0 | 1.01 |
| Phil Blunsom | 4 | 3130 | 152.18 |
| Andrew Markham | 5 | 519 | 48.34 |
| Niki Trigoni | 6 | 1160 | 85.23 |