Automated Removal of Planar Clutter from 3D Point Clouds for Improving Industrial Object Recognition

Object recognition in three-dimensional point clouds is a new research topic in the field of computer vision. Numerous nuisances, such as noise, a varying density, and occlusion greatly increase the difficulty of 3D object recognition. An improved local feature descriptor is proposed to address these problems in this paper. At each feature point, a local reference frame is established by calculating a scatter matrix based on the geometric center and the weighted point-cloud density of its neighborhood, and an improved normal vector estimation method is used to generate a new signature of histograms of orientations (SHOT) local-feature descriptor. The geometric consistency and iterative closest point method realize 3D model recognition in the point-cloud scenes. The experimental results show that the proposed SHOT feature-extraction algorithm has high robustness and descriptiveness in the object recognition of 3D local descriptors in cluttered point-cloud scenes.

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Tracking of secondary and temporary objects in structural concrete work

Construction Innovation ◽

10.1108/ci-12-2012-0063 ◽

2014 ◽

Vol 14 (2) ◽

pp. 145-167 ◽

Cited By ~ 25

Author(s):

Yelda Turkan ◽

Frédéric Bosché ◽

Carl T. Haas ◽

Ralph Haas

Keyword(s):

Object Recognition ◽

Laser Scanning ◽

Real Life ◽

Point Clouds ◽

Earned Value ◽

Structural Concrete ◽

Content Type ◽

3D Point Clouds ◽

Bim Object ◽

The Impact

Purpose – Previous research has shown that “Scan-vs-BIM” object recognition systems, which fuse three dimensional (3D) point clouds from terrestrial laser scanning (TLS) or digital photogrammetry with 4D project building information models (BIM), provide valuable information for tracking construction works. However, until now, the potential of these systems has been demonstrated for tracking progress of permanent structural works only; no work has been reported yet on tracking secondary or temporary structures. For structural concrete work, temporary structures include formwork, scaffolding and shoring, while secondary components include rebar. Together, they constitute most of the earned value in concrete work. The impact of tracking secondary and temporary objects would thus be added veracity and detail to earned value calculations, and subsequently better project control and performance. The paper aims to discuss these issues. Design/methodology/approach – Two techniques for recognizing concrete construction secondary and temporary objects in TLS point clouds are implemented and tested using real-life data collected from a reinforced concrete building construction site. Both techniques represent significant innovative extensions of existing “Scan-vs-BIM” object recognition frameworks. Findings – The experimental results show that it is feasible to recognise secondary and temporary objects in TLS point clouds with good accuracy using the two novel techniques; but it is envisaged that superior results could be achieved by using additional cues such as colour and 3D edge information. Originality/value – This article makes valuable contributions to the problem of detecting and tracking secondary and temporary objects in 3D point clouds. The power of Scan-vs-BIM object recognition approaches to address this problem is demonstrated, but their limitations are also highlighted.

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Semantic Mapping for Autonomous Subsea Intervention

Sensors ◽

10.3390/s21206740 ◽

2021 ◽

Vol 21 (20) ◽

pp. 6740

Author(s):

Guillem Vallicrosa ◽

Khadidja Himri ◽

Pere Ridao ◽

Nuno Gracias

Keyword(s):

Object Recognition ◽

A Priori ◽

Laser Scanner ◽

Point Clouds ◽

Bayesian Techniques ◽

Semantic Map ◽

3D Point Clouds ◽

Feature Based ◽

Object Classes ◽

High Level

This paper presents a method to build a semantic map to assist an underwater vehicle-manipulator system in performing intervention tasks autonomously in a submerged man-made pipe structure. The method is based on the integration of feature-based slam and 3D object recognition using a database of a priori known objects. The robot uses dvl, pressure, and ahrs sensors for navigation and is equipped with a laser scanner providing non-coloured 3D point clouds of the inspected structure in real time. The object recognition module recognises the pipes and objects within the scan and passes them to the slam, which adds them to the map if not yet observed. Otherwise, it uses them to correct the map and the robot navigation if they were already mapped. The slam provides a consistent map and a drift-less navigation. Moreover, it provides a global identifier for every observed object instance and its pipe connectivity. This information is fed back to the object recognition module, where it is used to estimate the object classes using Bayesian techniques over the set of those object classes which are compatible in terms of pipe connectivity. This allows fusing of all the already available object observations to improve recognition. The outcome of the process is a semantic map made of pipes connected through valves, elbows and tees conforming to the real structure. Knowing the class and the position of objects will enable high-level manipulation commands in the near future.

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Compact covariance descriptors in 3D point clouds for object recognition

2012 IEEE International Conference on Robotics and Automation ◽

10.1109/icra.2012.6224740 ◽

2012 ◽

Cited By ~ 24

Author(s):

Duc Fehr ◽

Anoop Cherian ◽

Ravishankar Sivalingam ◽

Sam Nickolay ◽

Vassilios Morellas and ◽

...

Keyword(s):

Object Recognition ◽

Point Clouds ◽

3D Point Clouds

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