Robot-Assisted Floor Surface Profiling Using Low-Cost Sensors

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.

Increased plane identification precision with stereo identification

“…Matrix B * , the target matrix, encapsulates the construction task and contains the desired pose for each brick, as articulated in Eq. (22). The construction of an autonomous robot involves iteratively retrieving bricks from storage and accurately positioning them in the designated area until all bricks have been placed.…”

“…Construction robots often encounter plane identification problems, such as those found in wall painting robots [21], floor surface profiling robots [22], ground plane detection [23][24][25], and surface reconstruction [26,27]. Methods for plane extraction and parameter identification include random sample consensus (RANSAC) and its variants.…”

Autonomous recognition technology of carrier robot on various terrain environment

“…The accuracy of the terrain classification for the testing database in five class terrain recognition problem is 96.71%. RGB-D system has better timeliness and lower complexity, 7 but it is obviously affected by light, so it is not be used in the area of large vision. Binocular vision system has better anti-illumination performance, and the technology is more mature, is widely used in field scenes.…”