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pose estimation of a fast tumbling space noncooperative target using the time-of-flight camera

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2021年第16期235卷 2529-2546页

作者： Wang, Qi-shuai Cai, Guo-ping Shanghai Jiao Tong Univ Dept Engn Mech State Key Lab Ocean Engn 800 Dongchuan Rd Shanghai 200240 Peoples R China

This article proposes a pose estimation method for a fast tumbling space noncooperative target. The core idea of this method is to extract the target's body-fixed coordinate system by using the geometric characteristics of the target's point cloud and then by the body-fixed coordinate system to realize pose initialization and pose tracking of the target. In the extraction of the body-fixed coordinate system, a point cloud of the target, which can be obtained by a time-of-flight camera, can be divided into small plane point clouds firstly;then the geometric information of these plane point clouds can be utilized to extract the target's descriptive structures, such as the target surfaces and the solar panel supports;and finally the body-fixed coordinate system can be determined by the geometric characteristics of these structures. The body-fixed coordinate system obtained above can be used to determine the pose of consecutive point clouds of the target, that is, to realize the pose initialization and the pose tracking, and accumulated bias often emerges in the pose tracking. To mitigate the accumulated bias, a pose graph optimization method is adopted. In the end of this article, the performance of the proposed method is evaluated by numerical simulations. Simulation results show that when the distance between the target and the chaser is 10 m, the errors of the estimation results of the target's attitude and position are 0.025 degrees and 0.026 m, respectively. This means that the proposed method can achieve high-precision pose estimation of the noncooperative target.

关键词： Fast tumbling noncooperative target pose estimation geometric characteristics point cloud pose graph optimization

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Bathymetric Particle Filter SLAM With graph-Based Trajectory Update Method

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IEEE ACCESS 2021年 9卷 85464-85475页

作者： Zhang, Qianyi Li, Ye Ma, Teng Cong, Zheng Zhang, Wenjun Harbin Engn Univ Sci & Technol Underwater Vehicle Lab Harbin 150001 Peoples R China

A graph-based particle filter bathymetric simultaneous localization and mapping (BSLAM) method is proposed to solve the oscillation problem of the trajectories estimated by particles when using a low precise vehicle motion model and obtain accurate navigation results for autonomous underwater vehicles (AUVs). A graph-based trajectory update method is proposed to update the trajectories stored in particles before particle weighting to weaken the influence of the low precise odometer model on the particle trajectories. A particle weighting method based on submap matching is proposed to improve the robustness of the particle filter. Besides, a graph-based map generation method is proposed to solve the map selection problem of the particle filtering theory. The performance of the proposed method is demonstrated using a simulated dataset and a field dataset collected from a sea trial. The results show that the proposed method is more accurate and effective compared with a state-of-art particle filter BSLAM method.

关键词： Trajectory Particle filters Simultaneous localization and mapping Navigation Surfaces Particle measurements Memory management Autonomous underwater vehicle bathymetric simultaneous localization and mapping particle filter pose graph optimization

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pose-graph Neural Network Classifier for Global Optimality Prediction in 2D SLAM

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IEEE ACCESS 2021年 9卷 80466-80477页

作者： Azzam, Rana Kong, Felix H. Taha, Tarek Zweiri, Yahya Khalifa Univ Sci & Technol KU Ctr Autonomous Robot Syst KUCARS Abu Dhabi 127788 U Arab Emirates Univ Technol Sydney Fac Engn & Informat Technol Ctr Autonomous Syst Sydney NSW 2006 Australia Dubai Future Fdn Robot Lab Dubai U Arab Emirates Kingston Univ London Fac Sci Engn & Comp London SW15 3DW England

The ability to decide if a solution to a pose-graph problem is globally optimal is of high significance for safety-critical applications. Converging to a local-minimum may result in severe estimation errors along the estimated trajectory. In this paper, we propose a graph neural network based on a novel implementation of a graph convolutional-like layer, called poseConv, to perform classification of pose-graphs as optimal or sub-optimal. The operation of poseConv required incorporating a new node feature, referred to as cost, to hold the information that the nodes will communicate. A training and testing dataset was generated based on publicly available bench-marking pose-graphs. The neural classifier is then trained and extensively tested on several subsets of the pose-graph samples in the dataset. Testing results have proven the model's capability to perform classification with 92 - 98% accuracy, for the different partitions of the training and testing dataset. In addition, the model was able to generalize to previously unseen variants of pose-graphs in the training dataset. Our method trades a small amount of accuracy for a large improvement in processing time. This makes it faster than other existing methods by up-to three orders of magnitude, which could be of paramount importance when using computationally-limited robots overseen by human operators.

关键词： Simultaneous localization and mapping optimization Training Testing Trajectory Uncertainty Programming pose graph optimization global optimality graph neural network simultaneous localization and mapping

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Dual-function depth camera array for inline 3D reconstruction of complex pipelines

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AUTOMATION IN CONSTRUCTION 2023年第1期152卷

作者： Shang, Zhexiong Shen, Zhigang Univ Nebraska Lincoln Durham Sch Architectural Engn & Construct 122 NH Lincoln NE 68588 USA Univ Nebraska Lincoln Durham Sch Architectural Engn & Construct 113 NH Lincoln NE 68588 USA

Efficiently reconstructing dense, complete, and accurate 3D models of long and complex pipelines remains a challenging task. The complex pipelines often contain geometrically unknown curved segments that are difficult to reconstruct by the existing monocular or stereo camera-based approaches. This paper proposes a dual-function depth camera array (DF-DCA) based inline inspection (ILI) system for automated 3D reconstruction of under-ground pipelines that contain arbitrarily curved segments. This newly developed DCA system consists of a central-positioned depth camera for pipes routes estimation based on a reference-assisted vSLAM framework and four oblique-viewed depth cameras for pipe surface mapping. A subvolume-based approach is used to merge the RGBD sequences from the cameras to obtain an accurate 3D map of the pipeline without using DCA calibration. The results showed that the system can reconstruct high-quality 3D models of the pipes with <0.2% distance error for straight pipes, and with centimeter-level geometric accuracy for pipelines with both straight and curved sections.

关键词： Curved pipeline Inline inspection 3D reconstruction Depth camera vSLAM Camera registration Visual odometry Reference point pose graph optimization

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A pose estimation method of space non-cooperative target based on ORBFPFH SLAM

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OPTIK 2023年第1期286卷

作者： Yan, Zhiqiang Wang, Hongyuan Ze, Liuchuanjiang Ning, Qianhao Lu, Yinxi Harbin Inst Technol Elect Sci & Technol Postdoctoral Res Stn Harbin 150001 Peoples R China Harbin Inst Technol Space Opt Engn Res Ctr Harbin 150001 Peoples R China

In this paper, to improve the pose measurement accuracy of the Time-of-Flight (ToF) camera for space non-cooperative targets, a pose estimation method based on ORBFPFH Simultaneous Localization and Mapping (SLAM) with effective integration of strength and depth measurement information is proposed. The primary process of the method is as follows: First, a unique ORBFPFH Bag of Words (BoW) model for space targets is trained based on the ToF image dataset of spacecraft;Secondly, the pose of the non-cooperative space target is tracked based on the ORBFPFH feature and optimized using the pose graph;Then, based on the ORBFPFH BoW model, loop closure is detected to reduce the cumulative error. Finally, the proposed method is tested on the ToF image dataset: compared with the advanced ORB-SLAM2 algorithm, the proposed method has a minor translation error and rotation error on the test data, with a mean translation error less than 0.144 m and mean rotation error less than 0.642 degrees. The test results show that the proposed method can improve the pose estimation accuracy of space non-cooperative targets, achieve a better 3D point cloud reconstruction effect, and provide technical support for space applications such as rendezvous and docking of non-cooperative spacecraft. & COPY;2001 Elsevier Science. All rights reserved

关键词： Space non -cooperative target ORBFPFH SLAM pose estimation pose graph optimization BoW

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Early Bird: Loop Closures from Opposing Viewpoints for Perceptually-aliased Indoor Environments 16

Early Bird: Loop Closures from Opposing Viewpoints for Perce...

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16th International Joint Conference on Computer Vision, Imaging and Computer graphics Theory and Applications (VISIGRAPP) / 16th International Conference on Computer Vision Theory and Applications (VISAPP)

作者： Tourani, Satyajit Desai, Dhagash Parihar, Udit Singh Garg, Sourav Sarvadevabhatla, Ravi Kiran Milford, Michael Krishna, K. Madhava IIIT Hyderabad Robot Res Ctr Hyderabad India Queensland Univ Technol QUT Ctr Robot Brisbane Qld Australia IIIT Hyderabad Ctr Visual Informat Technol Hyderabad India

ISBN: (纸本)9789897584886

Significant recent advances have been made in Visual Place Recognition (VPR), feature correspondence and localization due to deep-learning-based methods. However, existing approaches tend to address, partially or fully, only one of two key challenges: viewpoint change and perceptual aliasing. In this paper, we present novel research that simultaneously addresses both challenges by combining deep-learnt features with geometric transformations based on domain knowledge about navigation on a ground-plane, without specialized hardware (e.g. downwards facing cameras, etc.). In particular, our integration of VPR with SLAM by leveraging the robustness of deep-learnt features and our homography-based extreme viewpoint invariance significantly boosts the performance of VPR, feature correspondence and pose graph sub-modules of the SLAM pipeline. We demonstrate a localization system capable of state-of-the-art performance despite perceptual aliasing and extreme 180-degree-rotated viewpoint change in a range of real-world and simulated experiments. Our system is able to achieve early loop closures that prevent significant drifts in SLAM trajectories.

关键词： Visual Place Recognition Homography Image Representation pose graph optimization Correspondences Detection

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Real-Time Lidar Odometry and Mapping with Loop Closure

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SENSORS 2022年第12期22卷 4373页

作者： Liu, Yonghui Zhang, Weimin Li, Fangxing Zuo, Zhengqing Huang, Qiang Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China Beijing Inst Technol Key Lab Biomimet Robots & Syst Minist Educ Beijing 100081 Peoples R China Beijing Adv Innovat Ctr Intelligent Robots & Syst Beijing 100081 Peoples R China

Real-time performance and global consistency are extremely important in Simultaneous Localization and Mapping (SLAM) problems. Classic lidar-based SLAM systems often consist of front-end odometry and back-end pose optimization. However, due to expensive computation, it is often difficult to achieve loop-closure detection without compromising the real-time performance of the odometry. We propose a SLAM system where scan-to-submap-based local lidar odometry and global pose optimization based on submap construction as well as loop-closure detection are designed as separated from each other. In our work, extracted edge and surface feature points are inserted into two consecutive feature submaps and added to the pose graph prepared for loop-closure detection and global pose optimization. In addition, a submap is added to the pose graph for global data association when it is marked as in a finished state. In particular, a method to filter out false loops is proposed to accelerate the construction of constraints in the pose graph. The proposed method is evaluated on public datasets and achieves competitive performance with pose estimation frequency over 15 Hz in local lidar odometry and low drift in global consistency.

关键词： real-time lidar odometry submap-based loop-closure detection pose graph optimization simultaneous localization and mapping (SLAM)

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CPL-SLAM: Efficient and Certifiably Correct Planar graph-Based SLAM Using the Complex Number Representation

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IEEE TRANSACTIONS ON ROBOTICS 2020年第6期36卷 1719-1737页

作者： Fan, Taosha Wang, Hanlin Rubenstein, Michael Murphey, Todd Northwestern Univ Dept Mech Engn Evanston IL 60201 USA Northwestern Univ Dept Comp Sci Evanston IL 60201 USA Northwestern Univ Dept Comp Sci & Mech Engn Evanston IL 60201 USA

In this article, we consider the problem of planar graph-based simultaneous localization and mapping (SLAM) that involves both poses of the autonomous agent and positions of observed landmarks. We present complex (CPL)-SLAM, an efficient and certifiably correct algorithm to solve planar graph-based SLAM using the complex number representation. We formulate and simplify planar graph-based SLAM as the maximum likelihood estimation on the product of unit complex numbers, and relax this nonconvex quadratic complex optimization problem to convex complex semidefinite programming (SDP). Furthermore, we simplify the corresponding complex SDP to Riemannian staircase optimization (RSO) on the complex oblique manifold that can be solved with the Riemannian trust region method. In addition, we prove that the SDP relaxation and RSO simplification are tight as long as the noise magnitude is below a certain threshold. The efficacy of this work is validated through applications of CPL-SLAM and comparisons with existing state-of-the-art methods on planar graph-based SLAM, which indicates that our proposed algorithm is capable of solving planar graph-based SLAM certifiably, and is more efficient in numerical computation and more robust to measurement noise than existing state-of-the-art methods. The C++ code for CPL-SLAM is available at https://***/MurpheyLab/CPL-SLAM.

关键词： Simultaneous localization and mapping optimization Autonomous agents Noise measurement Estimation Transmission line matrix methods certifiably correct algorithms pose graph optimization simultaneous localization and mapping

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Simultaneous Localization and Mapping in the Epoch of Semantics: A Survey

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2019年第3期17卷 729-742页

作者： Sualeh, Muhammad Kim, Gon-Woo Chungbuk Natl Univ Intelligent Robot Lab Control & Robot Engn Dept Cheongju Chungbuk South Korea

Simultaneous Localization and Mapping (SLAM) with an astonishing research history of over three decades has brought the concept to the door step of truly autonomous robotic systems. The concept has advanced beyond the map building and self-localization of robot on the map. On the other hand, the long-standing challenges pertaining to the provision of out of the box solution for range of conditions still needs to be addressed. However, the technological advancements in the area is steadily making its ways into industry. This paper surveys state-of-the-art SLAM and discuss the insights of existing methods. Starting with a classical definition of SLAM, a brief conceptual overview, and formulation of a standard SLAM system is carried out. While discussing the auxiliaries for solving SLAM, the influx of machine learning into SLAM is also addressed. Moreover, recent SLAM algorithms have been reviewed with a focus on emerging concept of semantics to augment the system. In this paper a taxonomy of recently developed SLAM algorithms with a detailed comparison metrics, is presented. Furthermore, open challenges, future directions and emerging research issues have also been laid down.

关键词： Bundle adjustment deep learning pose graph optimization semantics SLAM

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Using consecutive point clouds for pose and motion estimation of tumbling non-cooperative target

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ADVANCES IN SPACE RESEARCH 2019年第5期63卷 1576-1587页

作者： Li, Yipeng Wang, Yunpeng Xie, Yongchun Beijing Inst Control Engn Beijing 100190 Peoples R China Sci & Technol Space Intelligent Control Lab Beijing 100094 Peoples R China Tianjin Key Lab Micrograv & Hypograv Environm Sim Tianjin 300301 Peoples R China

This paper proposes a consecutive point clouds-based estimation scheme to resolve the state estimation problem for tumbling noncooperative space target during the rendezvous phase without a prior knowledge about its structure. First, a consistent pose estimation algorithm is realized by maintaining a global structure of the target that is reconstructed upon the pose graph optimization. Then an extend Kalman filter on Lie group is adopted to estimate the motion and inertia parameters of the target using the pose measurements of the point clouds. Finally, a semi-physical experimental study is carried out to evaluate the performance of the proposed estimation scheme. The result shows that the structure, motion and the inertia parameters can be estimated, and the total computation time is approximately linear with the number of point clouds. (C) 2018 COSPAR. Published by Elsevier Ltd. All rights reserved.

关键词： Non-cooperative target Consecutive point clouds Consistent pose estimation pose graph optimization EKF

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