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Mobile Robot Indoor Positioning Based on a Combination of Visual and Inertial Sensors

SENSORS 2019年第8期19卷 1773.-1773.页

作者： Gao, Mingjing Yu, Min Guo, Hang Xu, Yuan Nanchang Univ Inst Space Sci & Technol Nanchang 330031 Jiangxi Peoples R China Jiangxi Normal Univ Coll Comp Informat & Engn Nanchang 330022 Jiangxi Peoples R China Univ Jinan Sch Elect Engn Jinan 250022 Shandong Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250100 Shandong Peoples R China

Multi-sensor integrated navigation technology has been applied to the indoor navigation and positioning of robots. For the problems of a low navigation accuracy and error accumulation, for mobile robots with a single sensor, an indoor mobile robot positioning method based on a visual and inertial sensor combination is presented in this paper. First, the visual sensor (Kinect) is used to obtain the color image and the depth image, and feature matching is performed by the improved scale-invariant feature transform (sift) algorithm. Then, the absolute orientation algorithm is used to calculate the rotation matrix and translation vector of a robot in two consecutive frames of images. An inertial measurement unit (IMU) has the advantages of high frequency updating and rapid, accurate positioning, and can compensate for the Kinect speed and lack of precision. Three-dimensional data, such as acceleration, angular velocity, magnetic field strength, and temperature data, can be obtained in real-time with an IMU. The data obtained by the visual sensor is loosely combined with that obtained by the IMU, that is, the differences in the positions and attitudes of the two sensor outputs are optimally combined by the adaptive fade-out extended Kalman filter to estimate the errors. Finally, several experiments show that this method can significantly improve the accuracy of the indoor positioning of the mobile robots based on the visual and inertial sensors.

关键词： robot positioning visual sensor inertial sensor sift algorithm adaptive fade-out extended Kalman filter

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Research on Vehicle Detection Technology Based on sift Feature

Research on Vehicle Detection Technology Based on SIFT Featu...

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8th IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC)

作者： Tu Yawen Guo Jinxu Wuhan Univ Technol Coll Informat Engn Wuhan Hubei Peoples R China

ISBN: (纸本)9781538657744

To aim at the problem of vehicle detection in the process of vehicle identification, to propose a method that identifies the possible regions in the image by collecting image classifier samples, using sift algorithm to extract the eigenvalues, using svm classifier to classify, and using Gaussian pyramid in combination with sliding window. Then, to extract the results by the non-maximal suppression algorithm to improve the recognition rate of vehicle recognition. Based on this, to perform the experimental verification. The experimental process and the results show that the vehicle identification accuracy rate of the above method reaches 93.4%.

关键词： vehicle detection sift algorithm svm classifier Gaussian pyramid sliding window

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An IoT-Based Smart Shopping Cart Using the Contemporary Barcode Scanner 1

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Symposium-B on Intelligent Embedded Systems / 1st International Conference on NextGen Electronic Technologies - Silicon to Software (ICNETS2)

作者： Prithvish, V. N. Agrawal, Shraddha Alex, John Sahaya Rani VIT Univ Sch Elect Engn Chennai Campus Chennai Tamil Nadu India

ISBN: (数字)9789811085758

ISBN: (纸本)9789811085758;9789811085741

An efficacious prototyping of an intelligent Internet of Things (IoT)-based smart cart that primarily enhances the shopping experience of the customers as well as owners. The assembly of the smart cart consists of a contemporary barcode scanner from the present-day shopping scenario, feedback systems providing product weight and product imaging to avoid discrepancies, a cloud-based database and an embedded hardware to connect the above. The mechanism facilitates the customer to add and remove products throughout the shopping, updating the bill instantaneously, and further reflecting changes in the inventory. The framework incorporates a Parse cloud-based inventory to ease the managerial task at the owner end. Further focus has been also given on the proper placement of all the components on a cart for the efficient working of the shop, keeping user's ease of usage in mind.

关键词： Smart cart IoT Pars Beagle Bone Black TVS electronics BSC-101 star RTL-8188EU sift algorithm Load cell Barcode scanner

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Research on Fast Mosaic Method of UAV Aerial Images Based on Motion Trajectory

Research on Fast Mosaic Method of UAV Aerial Images Based on...

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作者： Sun Ye Wuhan Armament Sergeant School

An approach for fast stitching of UAV images based on motion trajectory is proposed for high speed aerial photogrammetry in unfamiliar areas with few control points. It uses the method based on images to determine the motion trajectory and rapidly select the UAV trajectory images. The sift-based image feature matching algorithm is applied to extract feature matching points. This approach can effectively solve image stitching problems, such as translation, rotation, scaling, illumination, occlusion and different viewing angles.

关键词： Unmanned Aerial Vehicle （UAV） fast image stitching motion trajectory sift algorithm

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Rapid Generation of Full View Image Based on Multi-camera 9

Rapid Generation of Full View Image Based on Multi-camera

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9th International Conference on Graphic and Image Processing (ICGIP)

作者： Zeng wen-Xian Li Wei Guo zhao-Kun Hebei Univ Econ & Business Informat & Technol Coll Shijiazhuang Hebei Peoples R China

ISBN: (数字)9781510617421

ISBN: (纸本)9781510617421

In order to obtain the full view image, proposing a fast method of generating full view images based on camera. the method based on multi-camera equipment and the spherical transform of the camera image acquisition using sphere transformation model. the improved sift algorithm is used to extract keypoints and match keypoints, and then calculating the transformation matrix based on the keypoints that filtered. finally, according to the projection matrix and transform matrix of each image, the images of subsequent cameras are spliced together. Using this method to generate full view images. the speed is fast and real-time.

关键词： full view image spherical transform sift algorithm image mosaic

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Research on Improved Image Registration algorithm Based on PROSAC algorithm

Research on Improved Image Registration Algorithm Based on P...

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2019第十一届数字图像处理国际会议

作者： Xiangyu Li Tianjie Lei Chun Zhao Jintao Huang Man Yuan Jiabao Wang China Institute of Water Resources and Hydropower Research(IWHR) North China University of Water Resources and Electric Power(NCWU)

In order to solve the problems of poor stability and multiple mismatching points in image registration,most scholars have used Random Sample Consensus(RANSAC) algorithm to optimize the matching ***,because of the randomness of the RANSAC algorithm itself,the matching algorithm has poor stability,low registration efficiency and poor *** solve this problem,an improved sift(Scale-invariant feature transform) image registration optimization algorithm based on PROSAC(Progressive Sampling Consensus) was *** experimental results showed that the proposed image registration optimization algorithm could effectively solve the problems of error matching and low efficiency in the process of image *** the same image to test,the average correct registration rate of the traditional RANSAC improved sift algorithm was 82%,and the average running time was 36 *** average correct registration rate of the sift image registration algorithm based on PROSAC improved sift image registration algorithm was 86.67%,the average running time was 26.51 seconds,and the running efficiency was increased by 36%.Therefore,the improved sift image registration algorithm based on PROSAC has higher robustness,can meet the needs of fast image mosaic,and has broad application prospects.

关键词： Image stitching sift algorithm RANSAC algorithm PROSAC algorithm

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Research on the sift algorithm in Image Matching

Research on the SIFT Algorithm in Image Matching

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2nd International Conference on Frontiers of Manufacturing and Design Science (ICFMD 2011)

作者： Cong, Yuan Chen, Xiaorong Li, Yiting Shanghai Univ Sci & Technol Shanghai 200093 Peoples R China Infinite Shanghai Commun Terminals Ltd Shanghai Peoples R China

ISBN: (纸本)9783037852828

sift feature matching algorithm is hot in the field of the currently feature matching research, its matching with the strong ability can deal with the translation, rotation, affine transformation occurring between images, and it also have a stable image feature matching ability to the images filmed at any angle. sift algorithm is adopted in this paper, matching feature point through the scale space, calculating the histogram of detecting feature point neighborhood of gradient direction characteristic vector and generation to sift eigenvector and the key points similarity measure. From different setting threshold, scale scaling, rotating, noise on the experiment, the experiment result proves this algorithm in the above aspects has good robustness, suitable for mass characteristic database of rapid, accurate matching.

关键词： Image Matching sift algorithm feature vector scale space

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An Improved sift algorithm for Binocular Vision

An Improved SIFT Algorithm for Binocular Vision

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2012 IEEE 11th International Conference on Signal Processing(ICSP 2012)

作者： Chuanjun Liu Hui Chen Lijie Gao School of information science and engineering Shandong University

ISBN: (纸本)9781467321969

In this paper we proposes an improved sift algorithm of feature points matching, in order to improve the matching efficiency and 'real-time'. First, we use the classical sift algorithm to extract image features, when formatting the feature descriptor, we use the gradient normalized;Second, since the sift feature points detected a large number of feature points, and each is 128-dimension vector, but for the matching algorithm based on the Euclidean minimum distance measure, we have to calculate the Euclidean distance of a point and all the feature points from the right image, then ascending sort, which leads to low efficiency of algorithm. Based on optical imaging theory and the theory of binocular vision, by the coordinate of each feature point of the first image, we narrow the search range of the second image from two directions of row and column, on the basis of maintaining the matching accuracy, we improve the efficiency of the algorithm.

关键词： sift algorithm feature point detection gradient normalized feature point matching

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Two Improved Methods of sift algorithm Combined with Harris

Two Improved Methods of SIFT Algorithm Combined with Harris

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24th Chinese Control and Decision Conference (CCDC)

作者： Shao Wei Li Na Sun Lijuan Sui Shulin Liu Xiangpeng Qingdao Univ Sci & Technol Coll Automat Elect & Engn Qingdao 266042 Peoples R China Qingdao Univ Sci & Technol Coll Mat Sci & Phys Qingdao 266061 Peoples R China

ISBN: (纸本)9781457720727

Due to the invariance of scale, rotation and illumination, sift algorithm is commonly used in image matching. According to Harris algorithm, two simplified methods of sift algorithm are developed to improve real-time efficiency and robustness of feature detection. In the stage of accurate keypoints localization, two improved methods are proposed to eliminate edge responses. One is the replacement of Hessian matrix by the local autocorrelation function of Harris;the other is using the measure of corner response function of the Hessian matrix. The experimental results show that the improved methods can increase the performance of sift algorithm.

关键词： sift algorithm Harris algorithm The Hessian matrix

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A Method of sift Simplifying and Matching algorithm Improvement

A Method of SIFT Simplifying and Matching Algorithm Improvem...

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2nd International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII)

作者： Zhou, Xinmin Wang, Kaiyuan Fu, Jian Wuhan Univ Technol Sch Automat Wuhan 430070 Hubei Peoples R China

ISBN: (纸本)9781509035755

Scale-invariant feature transform (sift) is a popular pattern recognition method in 2D-image because it can abstracts the features which are invariant to rotation, scale zooming, brightness changing. So it demonstrates a certain stability to objects subjected to view point changing and noise distribution. However, the dimension of the sift descriptors is too high, and its runtime is too long. Aiming at this disadvantage, this paper propose a new method to generate feature descriptor based on hierarchical region and treat different regions differently. Improved sift algorithm reclassificates descriptor generating regions, using a circular area divide into 2x2+ 1 sub-regions instead of rectangular area in original algorithm. In the feature matching stage, setting different thresholds to 2x2 fan-shaped regions and 1 annular region, to achieve retaining right matched points as much as possible while removing wrongs. Comparing with the sift algorithm in some aspects, experiment results show that in the condition of fuzzy, light, rotation and affine transformation, improved sift algorithm can accomplish image matching test well and matching speed significantly improved.

关键词： sift algorithm feature descriptor time complexity image matching

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