Frame Stitching in Indoor Environment Using Drone Captured Images

Ramaswamy, Akshaya; Gubbi, Jayavardhana; Raj, Rishin; Purushothaman, Balamuralidhar

doi:10.1109/icip.2018.8451671

Cited by 10 publications

(2 citation statements)

References 13 publications

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“…New developments propose mixing features' inertial navigation systems (INS) in order to improve efficiency or time processing. An indoor application for SIFT and INS is proposed by [106] for camera pose estimation, improving stitch drone-captured indoor video frames. Pose estimation can be achieved by INS to calculate the relation between image frames captured by the UAV to select the most related and reduce the number of image stitching processes.…”

Section: Feature-based: Local Hybrid Transformationmentioning

confidence: 99%

Image Mosaicing Applied on UAVs Survey

et al. 2022

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The use of UAV (unmanned aerial vehicle) technology has allowed for advances in the area of robotics in control processes and application development. Such is the case of image processing, in which, by the use of aerial photographs taken by these aircrafts, it is possible to perform surveillance and monitoring tasks. As an example, we can mention the use of aerial photographs for the generation of panoramic images through the process of stitching images without losing image resolution. Some applications are photogrammetry and mapping, where the main problems to be solved are image alignment and ghosting images, for which different stitching techniques can be applied. These methodologies can be categorized into direct methods or feature-based methods. This paper aims to show an overview of the most frequently applied mosaicing techniques in UAVs by providing an introduction to those interested in developing in this area. For this purpose, a summary of the most applied techniques and their applications is given, showing the trend of the research field and the contribution of different countries over time.

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Section: Feature-based: Local Hybrid Transformationmentioning

confidence: 99%

Image Mosaicing Applied on UAVs Survey

et al. 2022

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“…Cho et al [13] proposed a framework that uses the Histogram of Gradient and Local Binary Pattern to extract the feature map of the warehouse and finally detect the two-dimensional location of QR codes by classifying them via a support vector machine (SVM). Ramaswamy et al [14] proposed the frame stitching method for images taken with the camera mounted on a drone to obtain data by restoring the corrupted context of images taken from the indoor warehouse. Anand et al [15] proposed a grid localization framework based on image-processing algorithms, such as thick line detection, using the drone-mounted low-cost camera for largescale warehouse system automation.…”

Section: Inventory Management Using Dronesmentioning

confidence: 99%

3D Position Estimation of Objects for Inventory Management Automation Using Drones

Yoon,

Kim,

Youn

et al. 2023

Applied Sciences

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With the recent development of drone technology, drones are being used in various fields. Drones have the advantage of being equipped with various devices to move freely and perform various tasks. In the field of inventory management, many studies have been conducted into management automation based on the drone. Drones scan a marker, such as a quick response code (QR code), attached to the shelves to obtain location information of the shelves on which the inventory is loaded. At the same time, drones perform inventory management by scanning the marker attached to the inventory to obtain inventory information. However, unlike indoor warehouses, where grids or shelves are well-defined, a storage yard is not fixed in the location where the inventory is stored. It is difficult to recognize the loading position from the marker for a storage yard without shelves and grids. Furthermore, the loading position of the inventory is not fixed. For the automation of inventory management of warehouses where shelves and grids are undefined, this paper proposes a framework that estimates the inventory 3D position in the video frame based on a deep learning model. The proposed framework uses the image segmentation model to detect and decode the marker in the video frame to estimate the 3D position of a drone and inventory. In addition, the estimated inventory 3D position is corrected using the continuity of the video frame. Experiment results on the video dataset verified that the proposed framework improved the 3D position estimation performance of the inventory. Consequently, efficient inventory management based on drones can be performed through the proposed framework for the 3D position estimation of inventory in all types of warehouses.

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