Natural corners-based SLAM in unknown indoor environment

Yan, Rui-Jun; Lim, Sungjin; Lee, Jiyeong; Han, Chang-Soo

doi:10.1109/urai.2012.6462989

Cited by 10 publications

(6 citation statements)

References 3 publications

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“…However, this problem is very complicated, because robots construct maps, but robots need maps to locate themselves. To overcome the problems with mapping an unknown environment and locating locations, SLAM (Simultaneous localization and mapping) algorithm is one of the important solutions proposed to solve this problem based on sensors such as laser-scanning radar [4,5]. Balasuriya et al submitted a workshop to design and navigated a robot autonomously based on Robot Operating System (ROS) [6].…”

Section: Map Construction and Positioningmentioning

confidence: 99%

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A Novel Automatic Meal Delivery System

Lin¹,

Siao²,

Chien³

et al. 2021

Intelligent Automation &Amp; Soft Computing

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Since the rapid growth of the Fourth Industrial Revolution (or Industry 4.0), robots have been widely used in many applications. In the catering industry, robots are used to replace people to do routine jobs. Because meal is an important part of the catering industry, we aim to design and develop a robot to deliver meals for saving cost and improving a restaurant's performance in this paper. However, for the existing meal delivery system, the guests must make their meals by themselves. To let the food delivery system become more user-friendly, we integrate an automatic guided vehicle (AGV) and a robotic arm to deliver a meal and put it on a table efficiently. The system consists of four phases. In the first stage, since most food feeding environments are indoors, we must build a map of an environment for a robot and use a laser sensor to construct a surrounding object's real science. The second stage is global planning. The map is used for indoor navigation by computing the shortest path from the starting point to the map's end point. The third phase is regional planning for obstacle avoidance, immediate avoidance, and re-planning of obstacles on the path. The last stage is how to control a robot arm to grab and put a meal on a table. We use an icon as the hint of the height of a table and operate the arm rotation angle according to this icon's position. Our system provides the concept of an AGV combined with a robotic arm and provides a service for the food delivery system. In this way, waiters do not spend time dealing with meals and guests can automatically obtain their meals. The result shows that the proposed system will be helpful for the robot industry and the catering industry.

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Section: Map Construction and Positioningmentioning

confidence: 99%

“…4, we can use Eqs. (3)(4)(5) to compute linear speed, angular speed, and radius in a two-dimensional space. In the dynamic window, all curves outside this dynamic window cannot be reached by the robot in the next step.…”

Section: Regional Planningmentioning

confidence: 99%

A Novel Automatic Meal Delivery System

Lin¹,

Siao²,

Chien³

et al. 2021

Intelligent Automation &Amp; Soft Computing

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show abstract

“…Once the objects are identified, the next major task of a mobile robot is to localise the position of the robot on the map of the unknown environment. SLAM (Simultaneous Localisation and Mapping) is one of the most widely used algorithms that use sensors such as ultrasonic sensors or laser scanners to map an unfamiliar environment while localising the position of the robot on the map [5][6][7]. With the advancements in sensor technology, the use of SLAM in emergencies like disaster management has increased in the past few years [8].…”

Section: Introductionmentioning

confidence: 99%

Computer-Vision Based Object Detection and Recognition for Service Robot in Indoor Environment

Singh¹,

Kapoor²,

Thakur³

et al. 2022

Computers, Materials &Amp; Continua

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The near future has been envisioned as a collaboration of humans with mobile robots to help in the day-to-day tasks. In this paper, we present a viable approach for a real-time computer vision based object detection and recognition for efficient indoor navigation of a mobile robot. The mobile robotic systems are utilized mainly for home assistance, emergency services and surveillance, in which critical action needs to be taken within a fraction of second or real-time. The object detection and recognition is enhanced with utilization of the proposed algorithm based on the modification of You Look Only Once (YOLO) algorithm, with lesser computational requirements and relatively smaller weight size of the network structure. The proposed computer-vision based algorithm has been compared with the other conventional object detection/recognition algorithms, in terms of mean Average Precision (mAP) score, mean inference time, weight size and false positive percentage. The presented framework also makes use of the result of efficient object detection/recognition, to aid the mobile robot navigate in an indoor environment with the utilization of the results produced by the proposed algorithm. The presented framework can be further utilized for a wide variety of applications involving indoor navigation robots for different services.

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“…There are two reasons for the necessity of feature extraction. Firstly, due to the large storage space of raw sensor data, most of research has stored the geometrical parameters of extracted features instead of all the measured raw data [1][2]. On the other side, the features must be extracted and chosen as landmarks to correct the pose of mobile robot in SLAM research.…”

Section: Introductionmentioning

confidence: 99%

NURBS Curve Extraction from 2D Laser Sensor Data and 3D Simulated Data

Yan

Lee

Han³

2014

AMM

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This paper proposes a non-uniform rational B-spline (NURBS) curve extraction algorithm from 2D laser sensor data and 3D simulated data. In robot localization and mapping application, the raw sensor data cannot be stored due to its need of large storage space. However, only a small number of control points of NURBS curve are needed to be stored to recover the geometrical feature of raw data. To comprise the number of control points and accuracy of the extracted curve, global approximation method is adopted to minimize the error between the raw data and the extracted curve. In extraction process, all the weights are set as one firstly. After find the control points, a weight calculation method is developed to update the weight values. The NURBS curve with new weight has smaller error than with original weights. Finally, NURBS curve extraction results from real 2D laser sensor data and 3D simulated data are shown to check the feasibility of proposed algorithms.

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Natural corners-based SLAM in unknown indoor environment

Cited by 10 publications

References 3 publications

A Novel Automatic Meal Delivery System

A Novel Automatic Meal Delivery System

Computer-Vision Based Object Detection and Recognition for Service Robot in Indoor Environment

NURBS Curve Extraction from 2D Laser Sensor Data and 3D Simulated Data

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