Discrimination of active dynamic objects in stereo-based visual SLAM
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Title of host publication | Electronic Imaging |
| Subtitle of host publication | Image Processing: Algorithms and Systems XVI |
| Publisher | Society for Imaging Science and Technology |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2018 |
| Publication type | A4 Article in a conference publication |
| Event | IS&T International Symposium on Electronic Imaging - Duration: 28 Jan 2018 → 2 Feb 2018 |
Publication series
| Name | |
|---|---|
| ISSN (Electronic) | 2470-1173 |
Conference
| Conference | IS&T International Symposium on Electronic Imaging |
|---|---|
| Period | 28/01/18 → 2/02/18 |
Abstract
Over the years, the problem of simultaneous localization and mapping have been substantially studied. Effective and robust techniques have been developed for mapping and localizing in an unknown environment in real-time. However, the bulk of the work presumes that the environment under observation is composed of static objects. In this study, we propose an approach aimed at localizing and mapping an environment irrespective of the motion of the objects in the scene. A hard threshold based Iterative Closest Point algorithm is used to compute transformations between point clouds that are obtained from dense stereo matching. The dynamic entities along with system noise are identified and isolated in the form of outliers of the data correspondence step. A confidence metric is defined that helps in identifying and transitioning a 3D point from static to dynamic and vice versa. The results are then verified in a 2D domain with the aid of a modified Gaussian Mixture Model based motion estimation. The dynamic objects are segmented in 3D and 2D domains for any possible analysis and decision making. The results demonstrate that the proposed approach effectively eliminates noise and isolates the dynamic objects during the mapping of the environment.