Name
Abstract | ||
|---|---|---|
This paper presents a survey registration framework to assist in the recurrent inspection of a natural environment. Our framework coarsely aligns surveys at the image-level using visual simultaneous localization and mapping SLAM, and it registers images at the pixel-level using SIFT Flow, which enables rapid manual inspection. The variation in appearance of natural environments makes data association a primary challenge of this work. We discuss this and other challenges, including 1 alternative approaches for coarsely aligning surveys of a natural environment, 2 how to select which images to compare between two surveys, and 3 strategies to boost image registration accuracy. We evaluate each stage of our approach, emphasizing alignment accuracy and stability with respect to large seasonal variations. Our domain is lakeshore monitoring, in which an autonomous surface vessel surveyed a 1-km lakeshore 33 times in 14 months. Our results show that our framework precisely aligns a significant number of images between surveys captured up to roughly three months apart, often across marked variation in appearance. Using these results, a human was able to spot several changes between surveys that would have otherwise gone unnoticed. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1002/rob.21664 | J. Field Robotics |
Field | DocType | Volume |
Scale-invariant feature transform,Computer vision,Unmanned surface vehicle,Simulation,Data association,Artificial intelligence,Engineering,Simultaneous localization and mapping,Image registration | Journal | 34 |
Issue | ISSN | Citations |
1 | 1556-4959 | 3 |
PageRank | References | Authors |
0.36 | 22 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Griffith, Shane | 1 | 106 | 6.33 |
| Cédric Pradalier | 2 | 339 | 38.22 |