Monocular visual SLAM algorithm for autonomous vessel sailing in harbor area

2018 ◽  
Author(s):  
Sh. Wang ◽  
Yi. Zhang ◽  
F. Zhu
Keyword(s):  
Visual Slam ◽  
Slam Algorithm ◽  
Autonomous Vessel ◽  
Harbor Area

Rao-Blackwellized visual SLAM for small UAVs with vehicle model partition

2014 ◽  
Vol 41 (3) ◽  
pp. 266-274 ◽  
Author(s):  
Tianmiao Wang ◽  
Chaolei Wang ◽  
Jianhong Liang ◽  
Yicheng Zhang
Keyword(s):  
Particle Filter ◽  
Internal State ◽  
Factorization Method ◽  
Measurement Unit ◽  
Visual Slam ◽  
Vehicle Model ◽  
Theoretical Derivation ◽  
Content Type ◽  
Localization And Mapping ◽  
Slam Algorithm

Purpose – The purpose of this paper is to present a Rao–Blackwellized particle filter (RBPF) approach for the visual simultaneous localization and mapping (SLAM) of small unmanned aerial vehicles (UAVs). Design/methodology/approach – Measurements from inertial measurement unit, barometric altimeter and monocular camera are fused to estimate the state of the vehicle while building a feature map. In this SLAM framework, an extra factorization method is proposed to partition the vehicle model into subspaces as the internal and external states. The internal state is estimated by an extended Kalman filter (EKF). A particle filter is employed for the external state estimation and parallel EKFs are for the map management. Findings – Simulation results indicate that the proposed approach is more stable and accurate than other existing marginalized particle filter-based SLAM algorithms. Experiments are also carried out to verify the effectiveness of this SLAM method by comparing with a referential global positioning system/inertial navigation system. Originality/value – The main contribution of this paper is the theoretical derivation and experimental application of the Rao–Blackwellized visual SLAM algorithm with vehicle model partition for small UAVs.

scholarly journals Cooperative Visual SLAM based on Adaptive Covariance Intersection

2018 ◽  
Vol 2 (3) ◽  
pp. 151 ◽  
Author(s):  
Fethi Denim ◽  
Abdelkrim Nemra ◽  
Kahina Louadj ◽  
Abdelghani Boucheloukh ◽  
Mustapha Hamerlain ◽  
...  
Keyword(s):  
Stereo Vision ◽  
Variable Structure ◽  
Uncertain System ◽  
Visual Slam ◽  
Creative Commons ◽  
Localization And Mapping ◽  
Covariance Intersection ◽  
Position Data ◽  
Modelling Uncertainties ◽  
Slam Algorithm

Simultaneous localization and mapping (SLAM) is an essential capability for Unmanned Ground Vehicles (UGVs) travelling in unknown environments where globally accurate position data as GPS is not available. It is an important topic in the autonomous mobile robot research. This paper presents an Adaptive De-centralized Cooperative Vision-based SLAM solution for multiple UGVs, using the Adaptive Covariance Intersection (ACI) supported by a stereo vision sensor. In recent years, SLAM problem has gotten a specific consideration, the most commonly used approaches are the EKF-SLAM algorithm and the FAST-SLAM algorithm. The primary, which requires an accurate process and an observation model, suffers from the linearization problem. The last mentioned is not suitable for real-time implementation. In our work, the Visual SLAM (VSLAM) problem could be solved based on the Smooth Variable Structure Filter (SVSF) is proposed. This new filter is robust and stable to modelling uncertainties making it suitable for UGV localization and mapping problem. This new strategy retains the near optimal performance of the SVSF when applied to an uncertain system, it has the added benefit of presenting a considerable improvement in the robustness of the estimation process. All UGVs will add data features sorted by the ACI that estimate position on the global map. This solution gives, as a result, a large reliable map constructed by a group of UGVs plotted on it. This paper presents a Cooperative SVSF-VSLAM algorithm that contributes to solve the Adaptive Cooperative Vision SLAM problem for multiple UGVs. The algorithm was implemented on three mobile robots Pioneer 3-AT, using stereo vision sensors. Simulation results show eciency and give an advantage to our proposed algorithm, compared to the Cooperative EKF-VSLAM algorithm mainly concerning the noise quality.  This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

scholarly journals Research on the Application of Visual SLAM in Embedded GPU

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Tianji Ma ◽  
Nanyang Bai ◽  
Wentao Shi ◽  
Xi Wu ◽  
Lutao Wang ◽  
...  
Keyword(s):  
High Performance ◽  
Three Dimensional ◽  
Image Data ◽  
Operating Efficiency ◽  
Visual Slam ◽  
Mobile Platforms ◽  
Unknown Environment ◽  
Localization And Mapping ◽  
Slam Algorithm ◽  
Embedded Gpu

In the automatic navigation robot field, robotic autonomous positioning is one of the most difficult challenges. Simultaneous localization and mapping (SLAM) technology can incrementally construct a map of the robot’s moving path in an unknown environment while estimating the position of the robot in the map, providing an effective solution for robots to fully navigate autonomously. The camera can obtain corresponding two-dimensional digital images from the real three-dimensional world. These images contain very rich colour, texture information, and highly recognizable features, which provide indispensable information for robots to understand and recognize the environment based on the ability to autonomously explore the unknown environment. Therefore, more and more researchers use cameras to solve SLAM problems, also known as visual SLAM. Visual SLAM needs to process a large number of image data collected by the camera, which has high performance requirements for computing hardware, and thus, its application on embedded mobile platforms is greatly limited. This paper presents a parallelization method based on embedded hardware equipped with embedded GPU. Use CUDA, a parallel computing platform, to accelerate the visual front-end processing of the visual SLAM algorithm. Extensive experiments are done to verify the effectiveness of the method. The results show that the presented method effectively improves the operating efficiency of the visual SLAM algorithm and ensures the original accuracy of the algorithm.

scholarly journals Real-Time Photometric Calibrated Monocular Direct Visual SLAM

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3604 ◽  
Author(s):  
Peixin Liu ◽  
Xianfeng Yuan ◽  
Chengjin Zhang ◽  
Yong Song ◽  
Chuanzheng Liu ◽  
...  
Keyword(s):  
Real Time ◽  
Direct Method ◽  
Input Sequence ◽  
Visual Slam ◽  
Mapping Accuracy ◽  
Time Dynamic ◽  
Photometric Calibration ◽  
Localization And Mapping ◽  
Actual Environment ◽  
Slam Algorithm

To solve the illumination sensitivity problems of mobile ground equipment, an enhanced visual SLAM algorithm based on the sparse direct method was proposed in this paper. Firstly, the vignette and response functions of the input sequences were optimized based on the photometric formation of the camera. Secondly, the Shi–Tomasi corners of the input sequence were tracked, and optimization equations were established using the pixel tracking of sparse direct visual odometry (VO). Thirdly, the Levenberg–Marquardt (L–M) method was applied to solve the joint optimization equation, and the photometric calibration parameters in the VO were updated to realize the real-time dynamic compensation of the exposure of the input sequences, which reduced the effects of the light variations on SLAM’s (simultaneous localization and mapping) accuracy and robustness. Finally, a Shi–Tomasi corner filtered strategy was designed to reduce the computational complexity of the proposed algorithm, and the loop closure detection was realized based on the oriented FAST and rotated BRIEF (ORB) features. The proposed algorithm was tested using TUM, KITTI, EuRoC, and an actual environment, and the experimental results show that the positioning and mapping performance of the proposed algorithm is promising.

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