Name
Abstract | ||
|---|---|---|
In this paper, we present a novel RGB-D feature, RISAS, which is robust to Rotation, Illumination and Scale variations through fusing Appearance and Shape information. We propose a keypoint detector which is able to extract information rich regions in both appearance and shape using a novel 3D information representation method in combination with grayscale information. We extend our recent work on Local Ordinal Intensity and Normal Descriptor(LOIND), to further significantly improve its illumination, scale and rotation invariance using 1) a precise neighbourhood region selection method and 2) a more robust dominant orientation estimation. We also present a dataset for evaluation of RGB-D features, together with comprehensive experiments to illustrate the effectiveness of the proposed RGB-D feature when compared to SIFT, C-SHOT and LOIND. We also show the use of RISAS for point cloud alignment associated with many robotics applications and demonstrate its effectiveness in a poorly illuminated environment when compared with SIFT and ORB. |
Year | Venue | DocType |
|---|---|---|
2016 | CoRR | Journal |
Volume | Citations | PageRank |
abs/1603.04134 | 2 | 0.36 |
References | Authors | |
18 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoyang Li | 1 | 53 | 10.12 |
| Kanzhi Wu | 2 | 31 | 2.69 |
| Yong Liu | 3 | 213 | 45.82 |
| Ravindra Ranasinghe | 4 | 35 | 8.97 |
| Gamini Dissanayake | 5 | 2226 | 256.36 |
| Rong Xiong | 6 | 53 | 14.05 |