Data acquisition and image processing system for surface inspection

Bănică, Cosmin Karl; Paţurcă, Sanda Victorinne; Grigorescu, Sorin Dan; Stefan, Ana Maria

doi:10.1109/atee.2017.7905118

Cited by 7 publications

(3 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To verify the accuracy, three reference points of Outer Box triangulation properties of test object and CAD model was calculated as in (6). The comparison angle value determined the accuracy of the propose method.…”

Section: Accuracy Test For Partial Pose Estimationmentioning

confidence: 99%

“…Previous study presented various techniques on CAD image database development [5] using .stl data, image data acquisition for surface inspection [6], pre-processing techniques on image registration [7][8][9][10][11][12][13][14][15][16][17][18], features extraction such as Vote-based 3D shape recognition and registration [19], edges extraction [20], reflection symmetric [21], DoG-based detector presented by deriving scale-invariant mesh features for image registration [22], Local Procustes Regression (LPR) [23], Estimation-by-Completion (EbC) [24] and Customized three-dimensional template matching [25][26][27] In this paper, the study focus on object description, scaling and image registration method. The contribution of this paper two fold.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Partial pose estimation of 3D rigid object system using outer box method

Zulkoffli

Bakar

2020

IJEECS

View full text Add to dashboard Cite

<p><span>This article introduces a novel approach for identify partial pose estimation using template matching method. The algorithms performs 2D correlation matching on tested image to CAD database by using regional shape representation in order to get the similar object pose in CAD database. The descriptor named outer box method, it is useful for rescale or aligning object size in both different images of tested image and CAD database image and also provide interest point for segmentation in image registration stage. The proposed algorithm were experimentally shown to be robust to apply on scale changes, various complex shape, unstructured CAD database and mixed CAD model database. Last part, the identified pose and its retrieved pose angle was calculated and achieved high accuracyin range ±0.388˚ to ±1.471˚.</span></p>

show abstract

Section: Accuracy Test For Partial Pose Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Partial pose estimation of 3D rigid object system using outer box method

Zulkoffli

Bakar

2020

IJEECS

View full text Add to dashboard Cite

show abstract

“…The results were promising, showing the combination of SLAM and surface mapping to create accurate 3D models. Banica et al [11] used two sets of laser based imaging systems spatially correlated through the use of proximity sensors, odometry, and geolocation. Wen et al [12] investigated using a single 2D laser scanner to provide localization, whilst a RGB-D Sensor provided a 3D map of the environment.…”

Section: Introductionmentioning

confidence: 99%

Robot-Assisted Floor Surface Profiling Using Low-Cost Sensors

2019

View full text Add to dashboard Cite

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.

show abstract