Mixed Probability Inverse Depth Estimation Based on Probabilistic Graph Model

Liu, Wenlei; Wu, Sentang; Wang, Xiaolong; Li, Kai

doi:10.1109/access.2019.2920278

Cited by 3 publications

(5 citation statements)

References 28 publications

(29 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the actual inverse depth ρ, the Gaussian distribution precision λ and the scale coefficient of the correct data π , the probability distribution of the inverse depth measurement value x of the mixed model is as follows [30]:…”

Section: ) Estimation Of Pose and Inverse Depth Of Monocular Cameramentioning

confidence: 99%

Integrated Autonomous Relative Navigation Method Based on Vision and IMU Data Fusion

Liu

Wen

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

An integrated autonomous relative navigation method based on vision and IMU data fusion was proposed in this paper, which can improve the position accuracy effectively and has strong adaptability to environmental changes. Firstly, IMU pre-integration formula based on Runge Kutta method was derived, which can improve the pre-integration position accuracy and reduce the accumulated error effectively. Secondly, an inverse depth estimation method based on the mixed probability model was proposed during the system initialization process, which can improve the accuracy of camera depth estimation and provide better initial conditions for back-end optimization. Thirdly, a sliding window filtering method based on the probability graph was proposed, which can avoid repeated calculations and improve the sliding window filtering efficiency. Forthly, combined with the advantages of the direct method and the feature point method, a mixed re-projection optimization method was proposed, which can expand the application scope of the method and improve the optimization accuracy effectively. Finally, in the closed-loop optimization, a closedloop optimization method based on similar transformation is proposed to eliminate the accumulated error. In order to verify the environmental adaptability of the method and the impact of closed-loop detection on the relative navigation system, indoor and outdoor experiments were carried out with a hand-held camera and an IMU. EuRoC dataset was used in the experiments and the proposed method was compared with some classical methods. The experimental results showed that this method has high accuracy and robustness.

show abstract

Section: ) Estimation Of Pose and Inverse Depth Of Monocular Cameramentioning

confidence: 99%

Integrated Autonomous Relative Navigation Method Based on Vision and IMU Data Fusion

Liu

Wen

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, the accuracy and robustness of depth estimation are poor and vulnerable to external noise. In [31,32], in order to improve the accuracy and robustness, a mixed inverse depth estimation method based on a probability map is adopted.…”

Section: Front-end Processingmentioning

confidence: 99%

“…Figure 2 is the probability diagram of the Gauss-uniform mixture probability distribution model [32]. Assume that X={x1,⋯,xN} is the inverse depth value of the sensor observation, ρ={ρ1,⋯,ρN} is the real inverse depth value, π={π1,⋯,πN} is the proportion of good measurement data, 1- π is the proportion of interference, and the interference signal obeys the uniform distribution form, Ufalse[ρmin,ρmaxfalse].…”

Section: Front-end Processingmentioning

confidence: 99%

Incremental Pose Map Optimization for Monocular Vision SLAM Based on Similarity Transformation

Liu

et al. 2019

Sensors

Self Cite

View full text Add to dashboard Cite

The novel contribution of this paper is to propose an incremental pose map optimization for monocular vision simultaneous localization and mapping (SLAM) based on similarity transformation, which can effectively solve the scale drift problem of SLAM for monocular vision and eliminate the cumulative error by global optimization. With the method of mixed inverse depth estimation based on a probability graph, the problem of the uncertainty of depth estimation is effectively solved and the robustness of depth estimation is improved. Firstly, this paper proposes a method combining the sparse direct method based on histogram equalization and the feature point method for front-end processing, and the mixed inverse depth estimation method based on a probability graph is used to estimate the depth information. Then, a bag-of-words model based on the mean initialization K-means is proposed for closed-loop feature detection. Finally, the incremental pose map optimization method based on similarity transformation is proposed to process the back end to optimize the pose and depth information of the camera. When the closed loop is detected, global optimization is carried out to effectively eliminate the cumulative error of the system. In this paper, indoor and outdoor environmental experiments are carried out using open data sets, such as TUM and KITTI, which fully proves the effectiveness of this method. Closed-loop detection experiments using hand-held cameras verify the importance of closed-loop detection. This method can effectively solve the scale drift problem of monocular vision SLAM and has strong robustness.

show abstract

“…The Probabilistic Graph Model (PGM) is an effective method employing the probability graph to process multiple variables (W. Liu et al, 2019). The PGM-based recommendation algorithms (PGMRAs) identify users and items according to their latent spaces and perform clustering analysis to improve recommendation accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…The Probabilistic Graph Model (PGM) is an effective method employing the probability graph to process multiple variables (W. Liu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Graph Model Based Recommendation Algorithm for Material Selection in Self-directed Learning

Qiu,

Cui

2024

Sage Open

View full text Add to dashboard Cite

Faced the vast amount of information, choosing the appropriate materials is a prerequisite for effective self-directed learning. The recommendation algorithm is a kind of intelligent technology that can accurately locate the required information which the users care about most. However, many recommendation techniques experience can not be trained adequately in scenarios with small sample data and extremely sparse ratings. Moreover, DLRAs (Deep learning based Recommendation Algorithms) require high hardware support. The probabilistic graph (PG) can effectively represent the implicit complex relations among nodes, but it still has the problem of sparse data sensitivity. Therefore, we propose a Matrix-Factorization-based Probabilistic Graph Model for Recommendation Algorithm (MF-PGMRA): By matrix-factorizing the sparse rating matrix, the users and items are mapped to the user/item spaces, respectively; We employ the inner product to data-enhance and overcome the problems of sparse data and cold start; Then, we build Probabilistic Graph to construct the “user-item” latent spaces and estimate the probability distribution based on expectation maximization (EM), so as to predict the ratings; Finally, we built a library management system with the recommendation module to highlight the benefits of MF-PGMRA for students’ subject learning. According to a questionnaire, we confirmed that the students are satisfied with the system from four aspects of speed, accuracy, usability and convenience, which can confirm that the library management system based on MF-PGMRA can efficiently and accurately recommend suitable materials for students from the huge amount of learning materials to improve students’ self-directed learning efficiency.

show abstract

Mixed Probability Inverse Depth Estimation Based on Probabilistic Graph Model

Cited by 3 publications

References 28 publications

Integrated Autonomous Relative Navigation Method Based on Vision and IMU Data Fusion

Integrated Autonomous Relative Navigation Method Based on Vision and IMU Data Fusion

Incremental Pose Map Optimization for Monocular Vision SLAM Based on Similarity Transformation

Probabilistic Graph Model Based Recommendation Algorithm for Material Selection in Self-directed Learning

Contact Info

Product

Resources

About