Three-dimensional mapping based on SIFT and RANSAC for mobile robot

Hu, Meiyu; Chen, Jianhui; Shi, Chengyu

doi:10.1109/cyber.2015.7287924

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…The creation of 3d maps is also one of the most important applications for image registration, where 3d mapping has been developed by the Swedish defense and security company Saab. Navigation by using 2D maps cannot meet the requirement for robot navigation because of the lack of navigation data [2]. Nowadays, there are large numbers of interest region detectors.…”

Section: Related Workmentioning

confidence: 99%

“…In this stage, we Search over multiple scales and image locations to identify potential interest points that are invariant to scale and orientation. the creation of the Gaussian scale-space pyramid is the first step by using the convolution of Gaussian functions to produce sequential blurred images (octave) as shown in equation ( 1) and equation (2). The parameter of n-Scales represents the number of scales inside the octave.…”

Section: Sift Algorithm Main Stagesmentioning

confidence: 99%

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Optimizing SIFT algorithm parameters for better matching UAV and satellite images

Elorabi,

Zekry,

Mohamed

2023

J. Phys.: Conf. Ser.

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Image registration has been increasingly employed in various applications such as target identification, 3D mapping, and motion tracking. The main idea of Image registration is aligning two or more images of the same scene captured from different viewpoints, at different times. Scale-invariant feature transform, SIFT, is considered one of the most robust algorithms used in image registration for extracting and matching features under different conditions. Using SIFT algorithm default parameters in Matching UAV and satellite Images provides unreliable results due to the nature of aerial images because the dynamic range is quite low. The number of extracted features depends on the image content and the selected parameters. In this paper we tuned SIFT parameters to get the best performance with aerial images, to increase the number of features (SM) and the correct match rate (CMR) which increases the efficiency of the process of registration. The algorithm is validated by matching a large number of aerial images taken by mini-UAV with satellite images for the same region.

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Section: Related Workmentioning

confidence: 99%

Section: Sift Algorithm Main Stagesmentioning

confidence: 99%

Optimizing SIFT algorithm parameters for better matching UAV and satellite images

Elorabi,

Zekry,

Mohamed

2023

J. Phys.: Conf. Ser.

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“…[36][37][38][39][40][41][42][43][44], few researchers have studied the global pooling layer. The global average pool is commonly used in most of state-of-the-art structures.…”

Section: Sparsity Analysismentioning

confidence: 99%

A CNN-SIFT Hybrid Pedestrian Navigation Method Based on First-Person Vision

et al. 2018

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Abstract:The emergence of new wearable technologies, such as action cameras and smart glasses, has driven the use of the first-person perspective in computer applications. This field is now attracting the attention and investment of researchers aiming to develop methods to process first-person vision (FPV) video. The current approaches present particular combinations of different image features and quantitative methods to accomplish specific objectives, such as object detection, activity recognition, user-machine interaction, etc. FPV-based navigation is necessary in some special areas, where Global Position System (GPS) or other radio-wave strength methods are blocked, and is especially helpful for visually impaired people. In this paper, we propose a hybrid structure with a convolutional neural network (CNN) and local image features to achieve FPV pedestrian navigation. A novel end-to-end trainable global pooling operator, called AlphaMEX, has been designed to improve the scene classification accuracy of CNNs. A scale-invariant feature transform (SIFT)-based tracking algorithm is employed for movement estimation and trajectory tracking of the person through each frame of FPV images. Experimental results demonstrate the effectiveness of the proposed method. The top-1 error rate of the proposed AlphaMEX-ResNet outperforms the original ResNet (k = 12) by 1.7% on the ImageNet dataset. The CNN-SIFT hybrid pedestrian navigation system reaches 0.57 m average absolute error, which is an adequate accuracy for pedestrian navigation. Both positions and movements can be well estimated by the proposed pedestrian navigation algorithm with a single wearable camera.

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Co-simulation of Omnidirectional Mobile Platform Based on Fuzzy Control

Zuo

Wang

et al. 2019

Intelligent Robotics and Applications

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Three-dimensional mapping based on SIFT and RANSAC for mobile robot

Cited by 7 publications

References 10 publications

Optimizing SIFT algorithm parameters for better matching UAV and satellite images

Optimizing SIFT algorithm parameters for better matching UAV and satellite images

A CNN-SIFT Hybrid Pedestrian Navigation Method Based on First-Person Vision

Co-simulation of Omnidirectional Mobile Platform Based on Fuzzy Control

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