scholarly journals Sparse Pose Graph Optimization in Cycle Space

2021 ◽  
pp. 1-20
Author(s):  
Fang Bai ◽  
Teresa Vidal-Calleja ◽  
Giorgio Grisetti
Keyword(s):  
Cycle Space ◽  
Graph Optimization ◽  
Pose Graph Optimization

A Pose-Graph Optimization tool for MATLAB

2018 ◽  
Author(s):  
João Carlos Virgolino Soares ◽  
Marco Antonio Meggiolaro
Keyword(s):  
Graph Optimization ◽  
Pose Graph Optimization

scholarly journals RTLIO: Real-Time LiDAR-Inertial Odometry and Mapping for UAVs

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3955
Author(s):  
Jung-Cheng Yang ◽  
Chun-Jung Lin ◽  
Bing-Yuan You ◽  
Yin-Long Yan ◽  
Teng-Hu Cheng
Keyword(s):  
Feedback Control ◽  
Real Time ◽  
High Frequency ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Sampling Rate ◽  
Outdoor Environment ◽  
Position Accuracy ◽  
Graph Optimization ◽  
Pose Graph Optimization

Most UAVs rely on GPS for localization in an outdoor environment. However, in GPS-denied environment, other sources of localization are required for UAVs to conduct feedback control and navigation. LiDAR has been used for indoor localization, but the sampling rate is usually too low for feedback control of UAVs. To compensate this drawback, IMU sensors are usually fused to generate high-frequency odometry, with only few extra computation resources. To achieve this goal, a real-time LiDAR inertial odometer system (RTLIO) is developed in this work to generate high-precision and high-frequency odometry for the feedback control of UAVs in an indoor environment, and this is achieved by solving cost functions that consist of the LiDAR and IMU residuals. Compared to the traditional LIO approach, the initialization process of the developed RTLIO can be achieved, even when the device is stationary. To further reduce the accumulated pose errors, loop closure and pose-graph optimization are also developed in RTLIO. To demonstrate the efficacy of the developed RTLIO, experiments with long-range trajectory are conducted, and the results indicate that the RTLIO can outperform LIO with a smaller drift. Experiments with odometry benchmark dataset (i.e., KITTI) are also conducted to compare the performance with other methods, and the results show that the RTLIO can outperform ALOAM and LOAM in terms of exhibiting a smaller time delay and greater position accuracy.

scholarly journals Research on Visual Positioning of a Roadheader and Construction of an Environment Map

2021 ◽  
Vol 11 (11) ◽  
pp. 4968
Author(s):  
Wentao Zhang ◽  
Guodong Zhai ◽  
Zhongwen Yue ◽  
Tao Pan ◽  
Ran Cheng
Keyword(s):  
Open Source ◽  
Optimal Estimation ◽  
Calculation Model ◽  
The Body ◽  
Elastic Band ◽  
Visual Positioning ◽  
Localization And Mapping ◽  
Graph Optimization ◽  
Random Sample Consensus ◽  
Pose Graph Optimization

The autonomous positioning of tunneling equipment is the key to intellectualization and robotization of a tunneling face. In this paper, a method based on simultaneous localization and mapping (SLAM) to estimate the body pose of a roadheader and build a navigation map of a roadway is presented. In terms of pose estimation, an RGB-D camera is used to collect images, and a pose calculation model of a roadheader is established based on random sample consensus (RANSAC) and iterative closest point (ICP); constructing a pose graph optimization model with closed-loop constraints. An iterative equation based on Levenberg–Marquadt is derived-, which can achieve the optimal estimation of the body pose. In terms of mapping, LiDAR is used to experimentally construct the grid map based on open-source algorithms, such as Gmapping, Cartographer, Karto, and Hector. A point cloud map, octree map, and compound map are experimentally constructed based on the open-source library RTAB-MAP. By setting parameters, such as the expansion radius of an obstacle and the updating frequency of the map, a cost map for the navigation of a roadheader is established. Combined with algorithms, such as Dijskra and timed-elastic-band, simulation experiments show that the combination of octree map and cost map can support global path planning and local obstacle avoidance.

scholarly journals Distributed mapping with privacy and communication constraints: Lightweight algorithms and object-based models

2017 ◽  
Vol 36 (12) ◽  
pp. 1286-1311 ◽  
Author(s):  
Siddharth Choudhary ◽  
Luca Carlone ◽  
Carlos Nieto ◽  
John Rogers ◽  
Henrik I Christensen ◽  
...  
Keyword(s):  
Distributed Algorithms ◽  
Field Experiments ◽  
Sensor Data ◽  
Test Facility ◽  
Two Stage ◽  
Noisy Information ◽  
Seidel Method ◽  
Object Based ◽  
Graph Optimization ◽  
Pose Graph Optimization

We consider the following problem: a team of robots is deployed in an unknown environment and it has to collaboratively build a map of the area without a reliable infrastructure for communication. The backbone for modern mapping techniques is pose graph optimization, which estimates the trajectory of the robots, from which the map can be easily built. The first contribution of this paper is a set of distributed algorithms for pose graph optimization: rather than sending all sensor data to a remote sensor fusion server, the robots exchange very partial and noisy information to reach an agreement on the pose graph configuration. Our approach can be considered as a distributed implementation of a two-stage approach that already exists, where we use the Successive Over-Relaxation and the Jacobi Over-Relaxation as workhorses to split the computation among the robots. We also provide conditions under which the proposed distributed protocols converge to the solution of the centralized two-stage approach. As a second contribution, we extend the proposed distributed algorithms to work with the object-based map models. The use of object-based models avoids the exchange of raw sensor measurements (e.g. point clouds or RGB-D data) further reducing the communication burden. Our third contribution is an extensive experimental evaluation of the proposed techniques, including tests in realistic Gazebo simulations and field experiments in a military test facility. Abundant experimental evidence suggests that one of the proposed algorithms (the Distributed Gauss–Seidel method) has excellent performance. The Distributed Gauss–Seidel method requires minimal information exchange, has an anytime flavor, scales well to large teams (we demonstrate mapping with a team of 50 robots), is robust to noise, and is easy to implement. Our field tests show that the combined use of our distributed algorithms and object-based models reduces the communication requirements by several orders of magnitude and enables distributed mapping with large teams of robots in real-world problems. The source code is available for download at https://cognitiverobotics.github.io/distributed-mapper/

scholarly journals Asynchronous and Parallel Distributed Pose Graph Optimization

2020 ◽  
Vol 5 (4) ◽  
pp. 5819-5826 ◽  
Author(s):  
Yulun Tian ◽  
Alec Koppel ◽  
Amrit Singh Bedi ◽  
Jonathan P. How
Keyword(s):  
Graph Optimization ◽  
Pose Graph Optimization

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.

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