Impact of trimet graph optimization topology on scalable networks

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.

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Research of Cartographer Graph Optimization Algorithm Based on Indoor Mobile Robot

Journal of Physics Conference Series ◽

10.1088/1742-6596/1651/1/012120 ◽

2020 ◽

Vol 1651 ◽

pp. 012120

Author(s):

Jiali Xu ◽

Di Wang ◽

Maosheng Liao ◽

Wenshuai Shen

Keyword(s):

Mobile Robot ◽

Optimization Algorithm ◽

Indoor Mobile Robot ◽

Graph Optimization

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

Sensors ◽

10.3390/s21123955 ◽

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.

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Majorization Minimization Methods for Distributed Pose Graph Optimization with Convergence Guarantees

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ◽

10.1109/iros45743.2020.9341063 ◽

2020 ◽

Author(s):

Taosha Fan ◽

Todd Murphey

Keyword(s):

Minimization Methods ◽

Graph Optimization ◽

Pose Graph Optimization

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

Applied Sciences ◽

10.3390/app11114968 ◽

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.

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