scholarly journals Spherical-Model-Based SLAM on Full-View Images for Indoor Environments

2018 ◽  
Vol 8 (11) ◽  
pp. 2268 ◽  
Author(s):  
Jianfeng Li ◽  
Xiaowei Wang ◽  
Shigang Li
Keyword(s):  
Spherical Model ◽  
Field Of View ◽  
Indoor Environments ◽  
Feature Points ◽  
Back Projection ◽  
Full Field ◽  
Full View ◽  
Model Based ◽  
Feature Points Matching ◽  
Localization And Mapping

As we know, SLAM (Simultaneous Localization and Mapping) relies on surroundings. A full-view image provides more benefits to SLAM than a limited-view image. In this paper, we present a spherical-model-based SLAM on full-view images for indoor environments. Unlike traditional limited-view images, the full-view image has its own specific imaging principle (which is nonlinear), and is accompanied by distortions. Thus, specific techniques are needed for processing a full-view image. In the proposed method, we first use a spherical model to express the full-view image. Then, the algorithms are implemented based on the spherical model, including feature points extraction, feature points matching, 2D-3D connection, and projection and back-projection of scene points. Thanks to the full field of view, the experiments show that the proposed method effectively handles sparse-feature or partially non-feature environments, and also achieves high accuracy in localization and mapping. An experiment is conducted to prove that the accuracy is affected by the view field.

scholarly journals Point-Plane SLAM Using Supposed Planes for Indoor Environments

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3795 ◽  
Author(s):  
Xiaoyu Zhang ◽  
Wei Wang ◽  
Xianyu Qi ◽  
Ziwei Liao ◽  
Ran Wei
Keyword(s):  
Fundamental Problem ◽  
Factor Graph ◽  
Indoor Environments ◽  
Feature Points ◽  
Estimation Errors ◽  
Depth Images ◽  
Localization And Mapping ◽  
Rgb Images ◽  
The Cost ◽  
Structural Parallel

Simultaneous localization and mapping (SLAM) is a fundamental problem for various applications. For indoor environments, planes are predominant features that are less affected by measurement noise. In this paper, we propose a novel point-plane SLAM system using RGB-D cameras. First, we extract feature points from RGB images and planes from depth images. Then plane correspondences in the global map can be found using their contours. Considering the limited size of real planes, we exploit constraints of plane edges. In general, a plane edge is an intersecting line of two perpendicular planes. Therefore, instead of line-based constraints, we calculate and generate supposed perpendicular planes from edge lines, resulting in more plane observations and constraints to reduce estimation errors. To exploit the orthogonal structure in indoor environments, we also add structural (parallel or perpendicular) constraints of planes. Finally, we construct a factor graph using all of these features. The cost functions are minimized to estimate camera poses and global map. We test our proposed system on public RGB-D benchmarks, demonstrating its robust and accurate pose estimation results, compared with other state-of-the-art SLAM systems.

scholarly journals RESEARCH ON SEMANTIC-ASSISTED SLAM IN COMPLEX DYNAMIC INDOOR ENVIRONMENT

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 353-359
Author(s):  
C. Li ◽  
Z. Kang ◽  
J. Yang ◽  
F. Li ◽  
Y. Wang
Keyword(s):  
Indoor Environment ◽  
Moving Objects ◽  
Solution Process ◽  
Dynamic Environments ◽  
Satellite System ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Feature Points ◽  
Localization And Mapping ◽  
Object Area

Abstract. Visual Simultaneous Localization and Mapping (SLAM) systems have been widely investigated in response to requirements, since the traditional positioning technology, such as Global Navigation Satellite System (GNSS), cannot accomplish tasks in restricted environments. However, traditional SLAM methods which are mostly based on point feature tracking, usually fail in harsh environments. Previous works have proven that insufficient feature points caused by missing textures, feature mismatches caused by too fast camera movements, and abrupt illumination changes will eventually cause state estimation to fail. And meanwhile, pedestrians are unavoidable, which introduces fake feature associations, thus violating the strict assumption that the unknown environment is static in SLAM. In order to ensure how our system copes with the huge challenges brought by these factors in a complex indoor environment, this paper proposes a semantic-assisted Visual Inertial Odometer (VIO) system towards low-textured scenes and highly dynamic environments. The trained U-net will be used to detect moving objects. Then all feature points in the dynamic object area need to be eliminated, so as to avoid moving objects to participate in the pose solution process and improve robustness in dynamic environments. Finally, the constraints of inertial measurement unit (IMU) are added for low-textured environments. To evaluate the performance of the proposed method, experiments were conducted on the EuRoC and TUM public dataset, and the results demonstrate that the performance of our approach is robust in complex indoor environments.

scholarly journals Feasibility of peripheral OCT imaging using a novel integrated SLO ultra-widefield imaging swept-source OCT device

2021 ◽  
Author(s):  
Simrat K. Sodhi ◽  
John Golding ◽  
Carmelina Trimboli ◽  
Netan Choudhry
Keyword(s):  
Posterior Pole ◽  
Field Of View ◽  
Retinal Diseases ◽  
Swept Source ◽  
Full Field ◽  
Swept Source Oct ◽  
Oct Imaging ◽  
Field Device ◽  
Areas Of Interest ◽  
Retinal Pathologies

Abstract Purpose To describe the feasibility of peripheral OCT imaging in retinal diseases using a novel full-field device. Methods A total of 134 consecutive eyes were referred and imaged on the Optos Silverstone swept-source OCT (SS-OCT) (Optos PLC; Dunfermline, UK). Scanning laser ophthalmoscope (SLO) images and the associated SS-OCT images were obtained in the posterior pole, mid-periphery or far periphery based on the nature of the referral and on new areas of interest observed in the optomap images at the time of imaging. Results A total of 134 eyes (96 patients) were enrolled in the study. One hundred and twenty-five eyes (91 patients) with 38 retinal pathologies were prospectively assessed and 9 eyes (5 patients) were excluded due to incomplete image acquisition. The average age of the subjects was 54 years (range 21–92 years). Thirty-nine out of 125 eyes (31%) had macular pathologies. Eighty-six out of 125 eyes (69%) had peripheral only pathologies, an area which cannot be visualized by standard OCT devices with a 50 degree field-of-view. Conclusions The ability to capture peripheral pathologies using an integrated SLO-UWF imaging with full-field swept-source provided high-grade anatomical insight that confirmed the medical and surgical management in a majority of cases. Its use in the mid- and far periphery provides a holistic clinical picture, which can potentially aid in the understanding of various retinal pathologies.

scholarly journals FSD-BRIEF: A Distorted BRIEF Descriptor for Fisheye Image Based on Spherical Perspective Model

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1839
Author(s):  
Yutong Zhang ◽  
Jianmei Song ◽  
Yan Ding ◽  
Yating Yuan ◽  
Hua-Liang Wei
Keyword(s):  
Feature Matching ◽  
Field Of View ◽  
Image Feature ◽  
Feature Points ◽  
Matching Process ◽  
Feature Descriptors ◽  
Coordinate Mapping ◽  
Matching Performance ◽  
Perspective Model ◽  
Attitude Matrix

Fisheye images with a far larger Field of View (FOV) have severe radial distortion, with the result that the associated image feature matching process cannot achieve the best performance if the traditional feature descriptors are used. To address this challenge, this paper reports a novel distorted Binary Robust Independent Elementary Feature (BRIEF) descriptor for fisheye images based on a spherical perspective model. Firstly, the 3D gray centroid of feature points is designed, and the position and direction of the feature points on the spherical image are described by a constructed feature point attitude matrix. Then, based on the attitude matrix of feature points, the coordinate mapping relationship between the BRIEF descriptor template and the fisheye image is established to realize the computation associated with the distorted BRIEF descriptor. Four experiments are provided to test and verify the invariance and matching performance of the proposed descriptor for a fisheye image. The experimental results show that the proposed descriptor works well for distortion invariance and can significantly improve the matching performance in fisheye images.

Full field of view super-resolution imaging based on two static gratings and white light illumination

2008 ◽  
Vol 47 (17) ◽  
pp. 3080 ◽  
Author(s):  
Javier García ◽  
Vicente Micó ◽  
Dan Cojoc ◽  
Zeev Zalevsky
Keyword(s):  
White Light ◽  
Super Resolution ◽  
Field Of View ◽  
Light Illumination ◽  
Full Field ◽  
Resolution Imaging ◽  
White Light Illumination

scholarly journals Real-Time Expanded Field-of-View for Minimally Invasive Surgery Using Multi-Camera Visual Simultaneous Localization and Mapping

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2106
Author(s):  
Ahmed Afifi ◽  
Chisato Takada ◽  
Yuichiro Yoshimura ◽  
Toshiya Nakaguchi
Keyword(s):  
Minimally Invasive Surgery ◽  
Minimally Invasive ◽  
Real Time ◽  
Invasive Surgery ◽  
Simultaneous Localization And Mapping ◽  
Field Of View ◽  
Time Dynamic ◽  
Matrix Estimation ◽  
Localization And Mapping

Minimally invasive surgery is widely used because of its tremendous benefits to the patient. However, there are some challenges that surgeons face in this type of surgery, the most important of which is the narrow field of view. Therefore, we propose an approach to expand the field of view for minimally invasive surgery to enhance surgeons’ experience. It combines multiple views in real-time to produce a dynamic expanded view. The proposed approach extends the monocular Oriented features from an accelerated segment test and Rotated Binary robust independent elementary features—Simultaneous Localization And Mapping (ORB-SLAM) to work with a multi-camera setup. The ORB-SLAM’s three parallel threads, namely tracking, mapping and loop closing, are performed for each camera and new threads are added to calculate the relative cameras’ pose and to construct the expanded view. A new algorithm for estimating the optimal inter-camera correspondence matrix from a set of corresponding 3D map points is presented. This optimal transformation is then used to produce the final view. The proposed approach was evaluated using both human models and in vivo data. The evaluation results of the proposed correspondence matrix estimation algorithm prove its ability to reduce the error and to produce an accurate transformation. The results also show that when other approaches fail, the proposed approach can produce an expanded view. In this work, a real-time dynamic field-of-view expansion approach that can work in all situations regardless of images’ overlap is proposed. It outperforms the previous approaches and can also work at 21 fps.

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