3D Mapping Aided GNSS-Based Cooperative Positioning Using Factor Graph Optimization

2019 ◽  
Author(s):  
Guohao Zhang
Keyword(s):  
Factor Graph ◽  
3D Mapping ◽  
Cooperative Positioning ◽  
Graph Optimization

scholarly journals Factor-graph-aided three-dimensional faster cooperative positioning algorithm

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Chengkai Tang ◽  
Jiaqi Liu ◽  
Yi Zhang ◽  
Xingxing Zhu ◽  
Lingling Zhang
Keyword(s):  
Three Dimensional ◽  
Factor Graph ◽  
Cooperative Positioning ◽  
Positioning Algorithm

scholarly journals ClusterSLAM: A SLAM backend for simultaneous rigid body clustering and motion estimation

2021 ◽  
Author(s):  
Jiahui Huang ◽  
Sheng Yang ◽  
Zishuo Zhao ◽  
Yu-Kun Lai ◽  
Shi-Min Hu
Keyword(s):  
Motion Estimation ◽  
Rigid Body ◽  
Iterative Scheme ◽  
Dynamic Environments ◽  
Rigid Bodies ◽  
Factor Graph ◽  
Agglomerative Clustering ◽  
Multiple Objects ◽  
Graph Optimization ◽  
Dynamic Objects

AbstractWe present a practical backend for stereo visual SLAM which can simultaneously discover individual rigid bodies and compute their motions in dynamic environments. While recent factor graph based state optimization algorithms have shown their ability to robustly solve SLAM problems by treating dynamic objects as outliers, their dynamic motions are rarely considered. In this paper, we exploit the consensus of 3D motions for landmarks extracted from the same rigid body for clustering, and to identify static and dynamic objects in a unified manner. Specifically, our algorithm builds a noise-aware motion affinity matrix from landmarks, and uses agglomerative clustering to distinguish rigid bodies. Using decoupled factor graph optimization to revise their shapes and trajectories, we obtain an iterative scheme to update both cluster assignments and motion estimation reciprocally. Evaluations on both synthetic scenes and KITTI demonstrate the capability of our approach, and further experiments considering online efficiency also show the effectiveness of our method for simultaneously tracking ego-motion and multiple objects.

scholarly journals Factor Graph-Assisted Distributed Cooperative Positioning Algorithm in the GNSS System

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3748 ◽  
Author(s):  
Chengkai Tang ◽  
Lingling Zhang ◽  
Yi Zhang ◽  
Houbing Song
Keyword(s):  
Smart Cities ◽  
Factor Graph ◽  
Positioning Error ◽  
Positioning Accuracy ◽  
Cooperative Positioning ◽  
Measured Range ◽  
Ranging Error ◽  
Positioning Algorithm ◽  
Range Error ◽  
Cooperative Nodes

The development of smart cities calls for improved accuracy in navigation and positioning services; due to the effects of satellite orbit error, ionospheric error, poor quality of navigation signals and so on, it is difficult for existing navigation technology to achieve further improvements in positioning accuracy. Distributed cooperative positioning technology can further improve the accuracy of navigation and positioning with existing GNSS (Global Navigation Satellite System) systems. However, the measured range error and the positioning error of the cooperative nodes exhibit larger reductions in positioning accuracy. In response to this question, this paper proposed a factor graph-aided distributed cooperative positioning algorithm. It establishes the confidence function of factor graphs theory with the ranging error and the positioning error of the coordinated nodes and then fuses the positioning information of the coordinated nodes by the confidence function. It can avoid the influence of positioning error and ranging error and improve the positioning accuracy of cooperative nodes. In the simulation part, the proposed algorithm is compared with a mainly coordinated positioning algorithm from four aspects: the measured range error, positioning error, convergence speed, and mutation error. The simulation results show that the proposed algorithm leads to a 30–60% improvement in positioning accuracy compared with other algorithms under the same measured range error and positioning error. The convergence rate and mutation error elimination times are only 1 / 5 to 1 / 3 of the other algorithms.

scholarly journals Factor Graph Weight Particles aided Distributed Underwater Cooperative Positioning Algorithm

2021 ◽  
Author(s):  
lingling zhang ◽  
baoguo yu ◽  
Chengkai Tang ◽  
yi zhang ◽  
Houbing Song
Keyword(s):  
Power Efficiency ◽  
High Efficiency ◽  
Low Complexity ◽  
Factor Graph ◽  
Network Nodes ◽  
Cooperative Positioning ◽  
Underwater Localization ◽  
Positioning Algorithm ◽  
Marine Exploration ◽  
Real Test

Abstract The growing scale of marine exploration requires high-resolution underwater localization, which necessitates cooperation among underwater network nodes, considering the channel complexity and power efficiency. In this paper, we proposed factor graph weight particles aided distributed underwater nodes cooperative positioning algorithm (WP-DUCP). It capitalized on the factor graph and sum-product algorithm to decompose the global optimization to the product of several local optimization functions. Combined with the Gaussian parameters to construct the weighted particles and to realize the belief transfer, it shows low complexity and high efficiency, suitable to the energy-restricted and communication distance-limited underwater networks. In terms of convergence, localization resolution, and computation complexity, we conducted the simulation and real-test with comparison to the existing co-localization methods. The results verified a higher resolution of the proposed method with no extra computation burden.

scholarly journals An Advanced Cooperative Positioning Algorithm Based on Improved Factor Graph and Sum-Product Theory for Multiple AUVs

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 67006-67017 ◽  
Author(s):  
Shiwei Fan ◽  
Ya Zhang ◽  
Chunyang Yu ◽  
Minghong Zhu ◽  
Fei Yu
Keyword(s):  
Factor Graph ◽  
Cooperative Positioning ◽  
Positioning Algorithm

scholarly journals Incremental 3-D pose graph optimization for SLAM algorithm without marginalization

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092530
Author(s):  
Feng Youyang ◽  
Wang Qing ◽  
Yang Gaochao
Keyword(s):  
Optimization Algorithm ◽  
Real World ◽  
Bayes Factor ◽  
Simulated Data ◽  
Schur Complement ◽  
Factor Graph ◽  
Data Sets ◽  
Loop Closure ◽  
Graph Optimization ◽  
Pose Graph Optimization

Pose graph optimization algorithm is a classic nonconvex problem which is widely used in simultaneous localization and mapping algorithm. First, we investigate previous contributions and evaluate their performances using KITTI, Technische Universität München (TUM), and New College data sets. In practical scenario, pose graph optimization starts optimizing when loop closure happens. An estimated robot pose meets more than one loop closures; Schur complement is the common method to obtain sequential pose graph results. We put forward a new algorithm without managing complex Bayes factor graph and obtain more accurate pose graph result than state-of-art algorithms. In the proposed method, we transform the problem of estimating absolute poses to the problem of estimating relative poses. We name this incremental pose graph optimization algorithm as G-pose graph optimization algorithm. Another advantage of G-pose graph optimization algorithm is robust to outliers. We add loop closure metric to deal with outlier data. Previous experiments of pose graph optimization algorithm use simulated data, which do not conform to real world, to evaluate performances. We use KITTI, TUM, and New College data sets, which are obtained by real sensor in this study. Experimental results demonstrate that our proposed incremental pose graph algorithm model is stable and accurate in real-world scenario.

scholarly journals Cooperative Positioning for Multi-AUVs Based on Factor Graph and Maximum Correntropy

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 153327-153337
Author(s):  
Shiwei Fan ◽  
Ya Zhang ◽  
Qiang Hao ◽  
Pan Jiang ◽  
Chunyang Yu ◽  
...  
Keyword(s):  
Factor Graph ◽  
Cooperative Positioning
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