Indoor Room Identify and Mapping with Virtual based SLAM using Furnitures and Household Objects Relationship based on CNNs

Maolanon, Pruttapon; Sukvichai, Kanjanapan; Chayopitak, Nattapon; Takahashi, Atsushi

doi:10.1109/ictemsys.2019.8695966

Cited by 15 publications

(7 citation statements)

References 6 publications

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“…YOLO perfectly detected whether or not a person in an image consisting of a person & a cup of coffee, is drinking coffee. Similarly the authors in [21] detect & classify household objects & furniture for localization & mapping using YOLO & SLAM running in a Robot Operating System (ROS) application.…”

Section: Related Workmentioning

confidence: 99%

“…The work in [32] utilises an object tracking system for dynamic path planning by predicting the future locations of the object. One of the notable works in robot mapping & navigation, SLAM, has been enhanced by the authors in [21] for household indoor environments. The work in [33] exploits sensor fusion of numerous odometer methods to develop a vision based localisation algorithm for curve tracking.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

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

View full text Add to dashboard Cite

show abstract

“…Deep learning object detection algorithms such as You Only Look Once (YOLO), Single Shot Detector (SSD), Faster R-CNN etc have been used in many robotic applications. In [4,5,6,7] object detection using pre-trained Convolutional Neural Networks (CNNs) integrated with visual SLAM to enhance the robotic capabilities. In this paper, three commonly used Lidar-based SLAM algorithms are evaluated in simulation environment.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Navigation of Indoor Service Robot with Object Recognition using Deep Learning

Abhijith¹,

S²

2022

IJRASET

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Indoor Service robots are becoming popular day by day. The task of service robot is to perform some predefined tasks originally done by human in houses, schools, offices etc. This paper proposes an autonomous indoor service robot system integrated with multi-sensors such as Lidar and Kinect for the smooth functioning of indoor service robots. Firstly, a map of the environment is created using Simultaneous Localization and Mapping (SLAM), a commonly used technique for creating map and positioning the robot at the same. In this paper different Lidar-based SLAM are compared and the best SLAM is chosen for creating 3D map for this system. For autonomous navigation, path planning plays a key role for fulfilling the task. A* algorithm is incorporated with this system to plan a global path. But global path planning algorithm fails to complete its task if any new or dynamic objects come to its path of navigation. To overcome such situations, a local path planning algorithm called Dynamic Window Approach (DWA) is added to this system. Another inevitable element in autonomous navigation is localization of robots in the map. Adaptive Monte Carlo Localization (AMCL) is the technique applied in the proposed system to localize the robot for the effective navigation of indoor service robot. To enhance the performance of service robot, YOLOv5 model is integrated with this system for real time object detection. The entire system is simulated in an open-source framework called Robot Operating System (ROS).

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“…Then estimate the position of user by efficient perspective-n-point (EPnP) algorithm. 9 Maolanon et al 10 improved SLAM algorithm with furniture detection CNN network so as to increase ability of robot. It combines mapping with object detection to let robot know where it is and which room it is in.…”

Section: Introductionmentioning

confidence: 99%

“…YOLOv3 tiny was chosen to the CNN detector. 10 Since the experiment of this study is carried out in the indoor space, an omnidirectional wheel mobile robot (WMR) is selected. Comparing the omnidirectional WMR to the ordinary moving robots, the omnidirectional WMR has more actions like right shift and left shift.…”

Section: Introductionmentioning

confidence: 99%

Application of 3D point cloud map and image identification to mobile robot navigation

Lin

Juang

2022

Measurement and Control

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This paper presents application of the combination of mapping, image identification, and indoor navigation for the omnidirectional wheeled mobile robot (WMR). The mapping part uses the point clouds converted from the depth map of the LiDAR camera to construct the indoor 3D point cloud map. Combining this map with the You Only Look Once ver.4 (YOLOv4) network, the door number and the doorknob can be displayed on the map by image identification. The navigation part applies 3D point cloud map, image, and depth map to an omnidirectional WMR so that it can know the current position and plan the path to the designated location. The proposed control scheme can make the robot perform indoor navigation and reach designated location.

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Indoor Room Identify and Mapping with Virtual based SLAM using Furnitures and Household Objects Relationship based on CNNs

Cited by 15 publications

References 6 publications

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

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

Autonomous Navigation of Indoor Service Robot with Object Recognition using Deep Learning

Application of 3D point cloud map and image identification to mobile robot navigation

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