Abstract
In this study, we focused on ceiling features, such as illuminators, emergency sign boards, and Wi-Fi routers, to cancel accumulated errors in simultaneous localization and mapping. First, point cloud data in indoor spaces are acquired using a time-of-flight camera. Second, ceiling surfaces are estimated from the acquired point cloud data with the random sample consensus algorithm. Third, ceiling features are estimated to be used for reference features. Then, gravity points of the estimated features are estimated to be used for reference points. Finally, an indoor environment map is generated with the reference points. Through our experiments in indoor environments, we clarified that our methodology can detect ceiling features to be used for reference points.
| Original language | English |
|---|---|
| Title of host publication | 37th Asian Conference on Remote Sensing, ACRS 2016 |
| Publisher | Asian Association on Remote Sensing |
| Pages | 443-448 |
| Number of pages | 6 |
| Volume | 1 |
| ISBN (Electronic) | 9781510834613 |
| Publication status | Published - 2016 |
| Event | 37th Asian Conference on Remote Sensing, ACRS 2016 - Colombo, Sri Lanka Duration: 2016 Oct 17 → 2016 Oct 21 |
Other
| Other | 37th Asian Conference on Remote Sensing, ACRS 2016 |
|---|---|
| Country/Territory | Sri Lanka |
| City | Colombo |
| Period | 16/10/17 → 16/10/21 |
Keywords
- Geo-referencing
- Indoor mapping
- Random point cloud
- SLAM
- TOF camera
ASJC Scopus subject areas
- Computer Networks and Communications