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Efficient Laser-Based 3D SLAM for Coal Mine Rescue Robots

IEEE ACCESS 2019年 7卷 14124-14138页

作者： Li, Menggang Zhu, Hua You, Shaoze Wang, Lei Tang, Chaoquan China Univ Min & Technol Sch Mech & Elect Engn Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Jiangsu Collaborat Innovat Ctr Intelligent Min Eq Xuzhou 221008 Jiangsu Peoples R China

An accurate description of laneway space with self-localization is a key issue when coal mine rescue robots (CMRRs) perform post-disaster exploration and rescue missions. The 3D simultaneous localization and mapping (SLAM) is an effective but time-critical and highly challenging task in complex laneway scenarios, especially after disasters. In this paper, we propose a novel real-time 3D SLAM based on normally distributed transform (NDT) that employs pose graph optimization and loop closure to further improve mapping consistency. We innovatively extract floors and walls in the laneway as plane nodes to construct landmark constraints, in addition to applying pose nodes from the lidar odometry via NDT. A lightweight and effective loop detection method is conducted using odometry with an appearance-based approach to building a globally consistent map. The proposed method was evaluated on a public dataset, and field tests in an underground coal mine were performed. Results indicate that our algorithm can achieve lower computational complexity and drift, which can provide pose estimation and environment description for CMRRs to realize remote control assistance and automatic navigation in coal mine rescue missions.

关键词： CMRRs SLAM NDT pose graph optimization

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Enabling Autonomous Operation of Micro Aerial Vehicles through GPS to GPS-Denied Transitions

Enabling Autonomous Operation of Micro Aerial Vehicles throu...

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作者： Jackson, James Scott Brigham Young University

学位级别：Ph.D.

Micro aerial vehicles and other autonomous systems have the potential to truly transform life as we know it, however much of the potential of autonomous systems remains unrealized because reliable navigation is still an unsolved problem with significant challenges. This dissertation presents solutions to many aspects of autonomous navigation. First, it presents ROSflight, a software and hardware architecture that allows for rapid prototyping and experimentation of autonomy algorithms on MAVs with lightweight, efficient flight control. Next, this dissertation presents improvements to the state-of-the-art in optimal control of quadrotors by utilizing the error- state formulation frequently utilized in state estimation. It is shown that performing optimal control directly over the error-state results in a vastly more computationally efficient system than competing methods while also dealing with the non-vector rotation components of the state in a principled way. In addition, real-time robust flight planning is considered with a method to navigate cluttered, potentially unknown scenarios with real-time obstacle avoidance. Robust state estimation is a critical component to reliable operation, and this dissertation focuses on improving the robustness of visual-inertial state estimation in a filtering framework by extending the state-of-the-art to include better modeling and sensor fusion. Further, this dissertation takes concepts from the visual-inertial estimation community and applies it to tightly-coupled GNSS, visual-inertial state estimation. This method is shown to demonstrate significantly more reliable state estimation than visual-inertial or GNSS-inertial state estimation alone in a hardware experiment through a GNSS-GNSS denied transition flying under a building and back out into open sky. Finally, this dissertation explores a novel method to combine measurements from multiple agents into a coherent map. Traditional approaches to this problem attempt to solve f

关键词： GPS degradation GPS denied navigation state estimation observability error state sensor fusion vision-aided INS consistency multirotor micro air vehicle indoor flight outdoor flight simultaneous localization and mapping (SLAM) pose graph optimization obstacle avoidance visual odometry Moving Horizon Estimation Pseudorange Linear Quadratic Regulator LQR Moving Horizon Estimation MHE Sliding Window optimization

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Cooperative Navigation of Fixed-Wing Micro Air Vehicles in GPS-Denied Environments

Cooperative Navigation of Fixed-Wing Micro Air Vehicles in G...

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作者： Ellingson, Gary James Brigham Young University

学位级别：Ph.D.

Micro air vehicles have recently gained popularity due to their potential as autonomous systems. Their future impact, however, will depend in part on how well they can navigate in GPS-denied and GPS-degraded environments. In response to this need, this dissertation investigates a potential solution for GPS-denied operations called relative navigation. The method utilizes keyframe-to-keyframe odometry estimates and their covariances in a global back end that represents the global state as a pose graph. The back end is able to effectively represent nonlinear uncertainties and incorporate opportunistic global constraints. The GPS-denied research community has, for the most part, neglected to consider fixed-wing aircraft. This dissertation enables fixed-wing aircraft to utilize relative navigation by accounting for their sensing requirements. The development of an odometry-like, front-end, EKF-based estimator that utilizes only a monocular camera and an inertial measurement unit is presented. The filter uses the measurement model of the multi-state-constraint Kalman filter and regularly performs relative resets in coordination with keyframe declarations. In addition to the front-end development, a method is provided to account for front-end velocity bias in the back-end optimization. Finally a method is presented for enabling multiple vehicles to improve navigational accuracy by cooperatively sharing information. Modifications to the relative navigation architecture are presented that enable decentralized, cooperative operations amidst temporary communication dropouts. The proposed framework also includes the ability to incorporate inter-vehicle measurements and utilizes a new concept called the coordinated reset, which is necessary for optimizing the cooperative odometry and improving localization. Each contribution is demonstrated through simulation and/or hardware flight testing. Simulation and Monte-Carlo testing is used to show the expected quality of the results.

关键词： GPS degradation GPS denied navigation state estimation observability extended Kalman filter sensor fusion vision-aided INS consistency multirotor fixed wing micro air vehicle indoor flight outdoor flight simultaneous localization and mapping place recognition loop closure pose graph optimization visual odometry cooperative estimation

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Resilient Underground Localization Using Magnetic Field Anomalies for Drilling Environment

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2018年第2期65卷 1377-1387页

作者： Park, Byeolteo Myung, Hyun Korea Adv Inst Sci & Technol Urban Robot Lab Daejeon 34141 South Korea

Underground localization is considered a key technology for controlling underground equipment, such as directional drilling equipment. However, most sensors for localization such as vision sensors, range sensors, and wireless sensors cannot be utilized in an underground environment. Moreover, vibration by the drill bit and distortion of the geomagnetic field by the surrounding material adversely affect localization performance. To accurately estimate the pose for directional drilling, a resilient underground localization method using concurrent normalized cross correlation (CNCC) and magnetic field vector is proposed in this paper. In the underground environment, a different magnetic field is distributed at each region. Since the drilling system moves only inside the borehole, the magnetic field distribution can be re-measured. Therefore, the constraint between the poses is detected by comparing the magnetic field distribution. A comparison method based on CNCC is proposed to resolve the ambiguity of the magnetic field. Moreover, to reduce the orientation error, magnetic field vector matching is used. The constraint can correct the pose estimation of the system using pose graph optimization. To validate the performance of the proposed method, two types of field tests are carried out in this paper.

关键词： Directional drilling magnetic field normalized cross correlation pose graph optimization underground localization

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Research on Simultaneous Localization and Mapping Algorithm Based on Monocular Vision 9th

Research on Simultaneous Localization and Mapping Algorithm ...

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9th China Satellite Navigation Conference (CSNC) - Location, Time of Augmentation

作者： Zhang, Han Lian, Baowang Lu, Hu Northwestern Polytech Univ Sch Elect & Informat Xian 710129 Shaanxi Peoples R China Air Force Engn Univ Sch Informat & Nav Xian 710129 Shaanxi Peoples R China

ISBN: (纸本)9789811300295;9789811300288

Simultaneous Localization and Mapping (SLAM) technology can make the robot in the unknown area positioning and building the map. Aiming at the problem of the indoor positioning in a small area, SLAM algorithm based on monocular camera was used. Feature based method was introduced to estimate the position of the robot and build the local map, and then BA (Bundle Adjustment) and graph optimization was used to optimize in the back-end. In order to solve the problem of scale drift in positioning, the bag-of-words (BoW) model and loop closing were used to reduce the system error, and an overall map was constructed at last. The experiment results show that the proposed algorithm can construct a sparse map in a small indoor scene, and the positioning error in the x direction reaches 0.0528 m, which meets the accuracy requirements and effectively reduces the scale drift.

关键词： SLAM Feature points Monocular pose graph optimization Loop closing

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Research on Simultaneous Localization and Mapping Algorithm Based on Monocular Vision

Research on Simultaneous Localization and Mapping Algorithm ...

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第九届中国卫星导航学术年会

作者： Han Zhang Baowang Lian Hu Lu

Simultaneous Localization and Mapping(SLAM) technology can make the robot in the unknown area positioning and building the *** at the problem of the indoor positioning in a small area,SLAM algorithm based on monocular camera was *** based method was introduced to estimate the position of the robot and build the local map,and then BA(Bundle Adjustment) and graph optimization was used to optimize in the *** order to solve the problem of scale drift in positioning,the bag-of-words(BoW) model and loop closing were used to reduce the system error,and an overall map was constructed at *** experiment results show that the proposed algorithm can construct a sparse map in a small indoor scene,and the positioning error in the x direction reaches 0.0534 m,which meets the accuracy requirements and effectively reduces the scale drift.

关键词： SLAM feature points monocular pose graph optimization loop closing

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An Effective Approach to SLAM toward Autonomous Operation for a Leg/Arm Composite Mobile Robot in Unknown Environment based on RGB-D Images

An Effective Approach to SLAM toward Autonomous Operation fo...

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Chinese Automation Congress (CAC)

作者： Li, Shuaijie Qin, Shiyin Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

ISBN: (纸本)9781538635247

In this paper, aneffective approach to Simultaneous Localization and Mapping (SLAM) based on RGB-D images is presented toward autonomous operation of a Leg/Arm Composite Mobile Robot (LACMR), in which depth intbrmation and its effective features are utilized sufficiently so as to overcome some malpractice in conventional methods and enhance the performance of SLAM. Our scheme can be narrated as follows. Firstly, in view of some feature-less regions in RGB image of operating scene, relevant stable and reliable depth information is employed to extract effective features. Secondly, the pose estimation and fine tuning is carried out by combining with features extracted from RGB-D images and proper successive matching of features. Thirdly the pose-graph optimization is conducted through the evaluation and selection of keyframes and loop closure detection to correct the drift and fine tuning the estimated pose. Finally, a dense 3D map is generated with keyframe information to serve for the autonomous operation. The accuracy and performance of the proposed SLAM approach is verified and validated by the map building and trajectory results with the TUM RGB-D dataset and our physical experiment results on a LACMR.

关键词： simultaneous localization and mapping (SLAM) autonomous operation feature extraction pose estimation pose graph optimization

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pose-Sequence-Based graph optimization Using Indoor Magnetic Field Measurements 3rd

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3rd International Conference on Robot Intelligence Technology and Applications

作者： Jung, Jongdae Myung, Hyun Korea Adv Inst Sci & Technol Dept Civil & Environm Engn 291 Daehak Ro Daejeon 305701 South Korea

ISBN: (纸本)9783319168418;9783319168401

In this paper we provide a method of handling loop closings in a simultaneous localization and mapping (SLAM) problem by employing indoor magnetic measurements and pose graph optimization. Since the magnetic field in indoor environments has unique spatial features, we can exploit these characteristics to generate the constraints for the pose graph-based SLAM. Specifically, whenever certain motion conditions are satisfied, a series of robot poses along with their magnetic measurements can be grouped into a sequence. A loop closing algorithm is then proposed based on the sequence and applied to the pose graph optimization. Experimental results show that the proposed SLAM system with only wheel encoders and a single magnetometer obtains comparable results with a reference-level SLAM system in terms of robot trajectory, by correctly detecting the loop closings.

关键词： SLAM magnetic field pose graph optimization

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Research of SLAM for Indoor Environment based on Kinect

Research of SLAM for Indoor Environment based on Kinect

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2016 2nd Workshop on Advanced Research and Technology in Industry Applications(WARTIA 2016)

作者： Di Liu Ri Pan Yajun Zhang School of Mechanical and Electrical Engineering Beijing University of Chemical Technology

ISBN: (纸本)9781510824430

The research of mobile robot's intelligence currently is a hot topic,and the basic and key technology to achieve the intelligence is Simultaneous Localization and Mapping(SLAM).In order to solve the problem of localization for mobile robots based on vision,A method of SLAM based on Kinect is ***,the successive images are captured by ***,the frame-to-frame alignments are performed based on ORB(Oriented FAST and Rotated BRIEF) features between frames,and the relative motion transformations are computed via the PnP ***,the key-frames are defined by the relative motion estimated between frames,then loop closure detection and global graph optimization are performed to efficiently decrease the accumulative error of poses and achieve a global consistence ***,the Octo Map are applied to represent the environment with less data *** results show the feasibility and effectiveness of this method.

关键词： SLAM Kinect ORB loop closure detection pose graph optimization

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A Co-Robotic Cane for Blind Navigation

A Co-Robotic Cane for Blind Navigation

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Ye, Cang Hong, Soonhac Qian, Xiangfei Univ Arkansas Dept Syst Engn Little Rock AR 72204 USA

ISBN: (纸本)9781479938407

This paper presents a new robotic navigation aid, called Co-Robotic Cane (CRC). The CRC uses a 3D camera for both pose Estimation (PE) and Object Recognition (OR) in an unknown indoor environment. The 6-DOF PE method determines the CRC's pose change by an egomotion estimation method, called Visual Range Odometry (VRO), and the Iterative Closest Point (ICP) algorithm and reduces the pose integration error by a pose graph optimization algorithm. The PE method does not require any prior knowledge of the environment. The OR method detects indoor structures (stairways, doorways, etc.) and objects (tables, computer monitors, etc.) by the Gaussian Mixture Models. Some of structures/objects (e.g., stairways) may be used as navigational waypoints and the others for obstacle avoidance. The CRC is a co-robot. It may detect human intent and collaborate with its user in performing a navigation task. The proposed CRC is the first in its kind.

关键词： robotic navigation aid robot pose estimation egomotion estimation pose graph optimization object recognition

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