Abstract
This paper presents the use of the ZED depth sensor in a robot-based painting application. The use of a stereo depth sensor is a very important factor in robotic applications, since it is both the initial and the essential step in a sequence of robotic operations, where the goal is to detect and extract the useful surface and objects or the obstacle on a wall that is not intended for painting. The ZED depth sensor was used for surface recording and navigation of our painting robot. Later, wall extraction was performed using simple image processing and morphological operations in a surface extraction algorithm. The goal was to use well-known, simple, and proven image processing operations in the algorithm to ensure both reliable and smooth operation of the robot’s vision system in an industrial environment. The experiments showed that the developed algorithm detects and extracts the wall successfully under various depth measurement conditions.
Highlights
This paper presents the use of the ZED depth sensor in a robot-based painting application
This paper introduces a procedure for window and obstacle detection from a depth image captured by a ZED depth sensor
TESTING RESULTS During the experiments, the algorithm was tested on various wall-window examples that were in the scope of the project requirements
Summary
This paper introduces a procedure for window and obstacle detection from a depth image captured by a ZED depth sensor The scope of this industrial research is the development of a simple image processing algorithm supported with information from a depth camera. This paper further develops the previous research on a specific robotic application based on depth image processing captured using a low-cost stereo depth camera. Almost no wall painting robots on the market are automated and the operator entirely controls the painting arm and decides what should be painted In this project, the customer has required automation using an image processing vision system. The goal is to develop a robust and simple computer vision algorithm to detect and extract mainly rectangular windows and large rectangular obstacles on the wall near the window area using depth images recorded with a stereo camera, as well as notifying the controlling system [1]. The acquired depth image and the point cloud of the environment model serve as resources for the robot to determine which surfaces to paint
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