Improvement of Door Recognition Algorithm using Lidar and RGB-D camera for Mobile Manipulator

Kim, Taehyeon; Kang, Min-Woo; Kang, Sumin; Kim, Donghan

doi:10.1109/sas54819.2022.9881249

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…After door detection, the method segments handles by color clustering and depth point extraction in the detected ROIs. Another YOLO-based approach was presented in [ 33 ], which uses YOLOv3 [ 10 ] to detect doors on a mobile robot navigating through a simulation of a building.…”

Section: Related Researchmentioning

confidence: 99%

Introduction to Door Opening Type Classification Based on Human Demonstration

Šimundić

Džijan

Pejić

et al. 2023

Sensors

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Opening doors and drawers will be an important ability for future service robots used in domestic and industrial environments. However, in recent years, the methods for opening doors and drawers have become more diverse and difficult for robots to determine and manipulate. We can divide doors into three distinct handling types: regular handles, hidden handles, and push mechanisms. While extensive research has been done on the detection and handling of regular handles, the other types of handling have not been explored as much. In this paper, we set out to classify the types of cabinet door handling types. To this end, we collect and label a dataset consisting of RGB-D images of cabinets in their natural environment. As part of the dataset, we provide images of humans demonstrating the handling of these doors. We detect the poses of human hands and then train a classifier to determine the type of cabinet door handling. With this research, we hope to provide a starting point for exploring the different types of cabinet door openings in real-world environments.

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Section: Related Researchmentioning

confidence: 99%

Introduction to Door Opening Type Classification Based on Human Demonstration

Šimundić

Džijan

Pejić

et al. 2023

Sensors

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“…The algorithm for determining whether point P(x p , y p , z p ) is located within the triangle V 1 V 2 V 3 is expressed in Eq. (15).…”

Section: Preprocessing Of Raw Point Cloud Datamentioning

confidence: 99%

“…RGB-D cameras, typically based on binocular vision principles [13, 14], are widely used in robots [15, 16], drones [17, 18], industrial production [19, 20] and more. Although their refresh rate is sometimes lower than LiDAR, RGB-D systems offer color information, providing additional scene details while remaining low-cost and easy to set up.…”

Section: Introductionmentioning

confidence: 99%

Increased plane identification precision with stereo identification

Zhao

2023

Robotica

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Stereo vision allows machines to perceive their surroundings, with plane identification serving as a crucial aspect of perception. The accuracy of identification constrains the applicability of stereo systems. Some stereo vision cameras are cost-effective, compact, and user-friendly, resulting in widespread use in engineering applications. However, identification errors limit their effectiveness in quantitative scenarios. While certain calibration methods enhance identification accuracy using camera distortion models, they rely on specific models tailored to a camera’s unique structure. This article presents a calibration method that is not dependent on any particular distortion model, capable of correcting plane position and orientation identified by any algorithm, provided that the identification error is biased. A high-precision mechanical calibration platform is designed to acquire accurate calibration data while using the same detected material in real measurement scenarios. Experimental comparisons confirm the efficacy of plane pose correction on PCL-RANSAC, with the average relative error of distance reduced by 5.4 times and the average absolute error of angle decreasing by 41.2%.

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Teaching a Robot Where Doors and Drawers Are and How To Handle Them

Cupec,

Vidović,

Šimundić

et al. 2023

2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

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We address the problem of teaching a service robot to detect doors and drawers in indoor environments. We propose a robust and accurate method in which a human demonstrates to the robot how to open doors and drawers that the robot is expected to operate in its future use. The proposed algorithm creates a model of a door or drawer from a sequence of RGB-D images and inserts it into an environment map. The model contains information about the size of the door panel or drawer front, as well as the position and orientation of the joint axis. This augmented environment map is then used by the robot to detect the target object in its environment and estimate its state.

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Improvement of Door Recognition Algorithm using Lidar and RGB-D camera for Mobile Manipulator

Cited by 3 publications

References 14 publications

Introduction to Door Opening Type Classification Based on Human Demonstration

Introduction to Door Opening Type Classification Based on Human Demonstration

Increased plane identification precision with stereo identification

Teaching a Robot Where Doors and Drawers Are and How To Handle Them

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