Abstract
This paper proposes a 3D object recognition method for non-coloured point clouds using point features. The method is intended for application scenarios such as Inspection, Maintenance and Repair (IMR) of industrial sub-sea structures composed of pipes and connecting objects (such as valves, elbows and R-Tee connectors). The recognition algorithm uses a database of partial views of the objects, stored as point clouds, which is available a priori. The recognition pipeline has 5 stages: (1) Plane segmentation, (2) Pipe detection, (3) Semantic Object-segmentation and detection, (4) Feature based Object Recognition and (5) Bayesian estimation. To apply the Bayesian estimation, an object tracking method based on a new Interdistance Joint Compatibility Branch and Bound (IJCBB) algorithm is proposed. The paper studies the recognition performance depending on: (1) the point feature descriptor used, (2) the use (or not) of Bayesian estimation and (3) the inclusion of semantic information about the objects connections. The methods are tested using an experimental dataset containing laser scans and Autonomous Underwater Vehicle (AUV) navigation data. The best results are obtained using the Clustered Viewpoint Feature Histogram (CVFH) descriptor, achieving recognition rates of , and , respectively, clearly showing the advantages of using the Bayesian estimation ( increase) and the inclusion of semantic information ( further increase).
Highlights
With the recent developments in the robotics industry there has been an increasing use of vehicle-mounted sensors
Over the last decade 3D point clouds have been widely used in computer vision and mobile robotics applications, opening the door to important but challenging tasks such as 3D object recognition [1,2,3,4,5,6] and semantic segmentation [7,8,9], which are core steps for scene understanding
The pressure sensor, the Attitude and Heading Reference System (AHRS), the Global Positioning System (GPS), the acoustic modem and the Doppler Velocity Log (DVL) provide measurements to estimate the pose of the vehicle
Summary
With the recent developments in the robotics industry there has been an increasing use of vehicle-mounted sensors. These sensors seek to provide useful information to the user, such as a clear perception of the environment, or provide more specific details such as obstacles to be avoided or objects to interact with. The outputs of these different sensors lead to different representations of the environment, depending on the sensor used and the task to be accomplished. Over the last decade 3D point clouds have been widely used in computer vision and mobile robotics applications, opening the door to important but challenging tasks such as 3D object recognition [1,2,3,4,5,6] and semantic segmentation [7,8,9], which are core steps for scene understanding
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