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Robot-Assisted Floor Surface Profiling Using Low-Cost Sensors
- Source :
- Remote Sensing; Volume 11; Issue 22; Pages: 2626, Remote Sensing, Vol 11, Iss 22, p 2626 (2019)
- Publication Year :
- 2019
- Publisher :
- MDPI AG, 2019.
-
Abstract
- Low cost and accurate 3D surface profiling can help in numerous industry applications including inspection tasks, cleaning, minimizing bumps in navigation of non-uniform terrain, aid navigation, and road/pavement condition analysis. However, most of the available systems are costly or inaccessible for widespread use. This research presents investigation into the capability of cheap and accessible sensors to capture the floor surface profile information. A differential drive robotic platform has been developed to perform testing and conduct the research. 2D localization methods are extrapolated into 3D for the floor capturing process. Two different types of sensors, a 2D laser scanner and an RGB-D camera, are used for comparison of the floor profile capture ability. The robotic system is able to successfully capture the floor surface profile of a number of different type floors such as carpet, asphalt, and a coated floor. A key finding is that the surface itself is a significant factor on the measured profile, i.e. dirt or differing materials can cause false height measurements. Overall the methodology has proved a successful real time solution for creating a point cloud of the floor surface.
- Subjects :
- surface map
0209 industrial biotechnology
floor surface profile
laser scanner
RGB-D camera
point cloud
mobile robot
robot operating system
Laser scanning
Computer science
Surface map
Science
Real-time computing
0211 other engineering and technologies
Point cloud
Terrain
02 engineering and technology
020901 industrial engineering & automation
021105 building & construction
Profiling (computer programming)
rgb-d camera
Process (computing)
Mobile robot
General Earth and Planetary Sciences
Robot
Subjects
Details
- ISSN :
- 20724292
- Volume :
- 11
- Database :
- OpenAIRE
- Journal :
- Remote Sensing
- Accession number :
- edsair.doi.dedup.....2d8825da9729d7997f0190ee2a989eef
- Full Text :
- https://doi.org/10.3390/rs11222626