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Inostroza F, Parra-Tsunekawa I, Ruiz-del-Solar J. Robust Localization for Underground Mining Vehicles: An Application in a Room and Pillar Mine. SENSORS (BASEL, SWITZERLAND) 2023; 23:8059. [PMID: 37836889 PMCID: PMC10574974 DOI: 10.3390/s23198059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 09/19/2023] [Indexed: 10/15/2023]
Abstract
Most autonomous navigation systems used in underground mining vehicles such as load-haul-dump (LHD) vehicles and trucks use 2D light detection and ranging (LIDAR) sensors and 2D representations/maps of the environment. In this article, we propose the use of 3D LIDARs and existing 3D simultaneous localization and mapping (SLAM) jointly with 2D mapping methods to produce or update 2D grid maps of underground tunnels that may have significant elevation changes. Existing mapping methods that only use 2D LIDARs are shown to fail to produce accurate 2D grid maps of the environment. These maps can be used for robust localization and navigation in different mine types (e.g., sublevel stoping, block/panel caving, room and pillar), using only 2D LIDAR sensors. The proposed methodology was tested in the Werra Potash Mine located at Philippsthal, Germany, under real operational conditions. The obtained results show that the enhanced 2D map-building method produces a superior mapping performance compared with a 2D map generated without the use of the 3D LIDAR-based mapping solution. The 2D map generated enables robust 2D localization, which was tested during the operation of an autonomous LHD, performing autonomous navigation and autonomous loading over extended periods of time.
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Affiliation(s)
- Felipe Inostroza
- Advanced Mining Technology Center, Universidad de Chile, Santiago 8370451, Chile; (F.I.); (I.P.-T.)
| | - Isao Parra-Tsunekawa
- Advanced Mining Technology Center, Universidad de Chile, Santiago 8370451, Chile; (F.I.); (I.P.-T.)
| | - Javier Ruiz-del-Solar
- Advanced Mining Technology Center, Universidad de Chile, Santiago 8370451, Chile; (F.I.); (I.P.-T.)
- Department of Electrical Engineering, Universidad de Chile, Santiago 8370451, Chile
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Nguyen TB, Murshed M, Choudhury T, Keogh K, Kahandawa Appuhamillage G, Nguyen L. A Depth-Based Hybrid Approach for Safe Flight Corridor Generation in Memoryless Planning. SENSORS (BASEL, SWITZERLAND) 2023; 23:7206. [PMID: 37631745 PMCID: PMC10458145 DOI: 10.3390/s23167206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 08/14/2023] [Accepted: 08/15/2023] [Indexed: 08/27/2023]
Abstract
This paper presents a depth-based hybrid method to generate safe flight corridors for a memoryless local navigation planner. It is first proposed to use raw depth images as inputs in the learning-based object-detection engine with no requirement for map fusion. We then employ an object-detection network to directly predict the base of polyhedral safe corridors in a new raw depth image. Furthermore, we apply a verification procedure to eliminate any false predictions so that the resulting collision-free corridors are guaranteed. More importantly, the proposed mechanism helps produce separate safe corridors with minimal overlap that are suitable to be used as space boundaries for path planning. The average intersection of union (IoU) of corridors obtained by the proposed algorithm is less than 2%. To evaluate the effectiveness of our method, we incorporated it into a memoryless planner with a straight-line path-planning algorithm. We then tested the entire system in both synthetic and real-world obstacle-dense environments. The obtained results with very high success rates demonstrate that the proposed approach is highly capable of producing safe corridors for memoryless local planning.
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Affiliation(s)
- Thai Binh Nguyen
- Institute of Innovation, Science and Sustainability, Federation University Australia, Churchill, VIC 3842, Australia; (T.B.N.); (T.C.); (K.K.); (G.K.A.)
| | - Manzur Murshed
- School of Information Technology, Deakin University, Burwood, VIC 3125, Australia;
| | - Tanveer Choudhury
- Institute of Innovation, Science and Sustainability, Federation University Australia, Churchill, VIC 3842, Australia; (T.B.N.); (T.C.); (K.K.); (G.K.A.)
| | - Kathleen Keogh
- Institute of Innovation, Science and Sustainability, Federation University Australia, Churchill, VIC 3842, Australia; (T.B.N.); (T.C.); (K.K.); (G.K.A.)
| | - Gayan Kahandawa Appuhamillage
- Institute of Innovation, Science and Sustainability, Federation University Australia, Churchill, VIC 3842, Australia; (T.B.N.); (T.C.); (K.K.); (G.K.A.)
| | - Linh Nguyen
- Institute of Innovation, Science and Sustainability, Federation University Australia, Churchill, VIC 3842, Australia; (T.B.N.); (T.C.); (K.K.); (G.K.A.)
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Wang S, Zhang H, Wang G. OMC-SLIO: Online Multiple Calibrations Spinning LiDAR Inertial Odometry. SENSORS (BASEL, SWITZERLAND) 2022; 23:s23010248. [PMID: 36616845 PMCID: PMC9824160 DOI: 10.3390/s23010248] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/19/2022] [Accepted: 12/22/2022] [Indexed: 06/12/2023]
Abstract
Light detection and ranging (LiDAR) is often combined with an inertial measurement unit (IMU) to get the LiDAR inertial odometry (LIO) for robot localization and mapping. In order to apply LIO efficiently and non-specialistically, self-calibration LIO is a hot research topic in the related community. Spinning LiDAR (SLiDAR), which uses an additional rotating mechanism to spin a common LiDAR and scan the surrounding environment, achieves a large field of view (FoV) with low cost. Unlike common LiDAR, in addition to the calibration between the IMU and the LiDAR, the self-calibration odometer for SLiDAR must also consider the mechanism calibration between the rotating mechanism and the LiDAR. However, existing self-calibration LIO methods require the LiDAR to be rigidly attached to the IMU and do not take the mechanism calibration into account, which cannot be applied to the SLiDAR. In this paper, we propose firstly a novel self-calibration odometry scheme for SLiDAR, named the online multiple calibration inertial odometer (OMC-SLIO) method, which allows online estimation of multiple extrinsic parameters among the LiDAR, rotating mechanism and IMU, as well as the odometer state. Specially, considering that the rotating and static parts of the motor encoder inside the SLiDAR are rigidly connected to the LiDAR and IMU respectively, we formulate the calibration within the SLiDAR as two separate sets of calibrations: the mechanism calibration between the LiDAR and the rotating part of the motor encoder and the sensor calibration between the static part of the motor encoder and the IMU. Based on such a SLiDAR calibration formulation, we can construct a well-defined kinematic model from the LiDAR to the IMU with the angular information from the motor encoder. Based on the kinematic model, a two-stage motion compensation method is presented to eliminate the point cloud distortion resulting from LiDAR spinning and platform motion. Furthermore, the mechanism and sensor calibration as well as the odometer state are wrapped in a measurement model and estimated via an error-state iterative extended Kalman filter (ESIEKF). Experimental results show that our OMC-SLIO is effective and attains excellent performance.
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Affiliation(s)
- Shuang Wang
- Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Southwest University of Science and Technology, Mianyang 621002, China
| | - Hua Zhang
- Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Southwest University of Science and Technology, Mianyang 621002, China
| | - Guijin Wang
- Shanghai AI Laboratory, Shanghai 200232, China
- Department of Electrical Engineering, Tsinghua University, Beijing 100089, China
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Chang Y, Ebadi K, Denniston CE, Ginting MF, Rosinol A, Reinke A, Palieri M, Shi J, Chatterjee A, Morrell B, Agha-mohammadi AA, Carlone L. LAMP 2.0: A Robust Multi-Robot SLAM System for Operation in Challenging Large-Scale Underground Environments. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3191204] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Yun Chang
- Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kamak Ebadi
- NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | | | | | - Antoni Rosinol
- Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Matteo Palieri
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, Italy
| | - Jingnan Shi
- Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Arghya Chatterjee
- Department of Mechanical Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
| | - Benjamin Morrell
- NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | | | - Luca Carlone
- Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA
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Song J, Lee J. Online Self-Calibration of 3D Measurement Sensors Using a Voxel-Based Network. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22176447. [PMID: 36080905 PMCID: PMC9460808 DOI: 10.3390/s22176447] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 08/22/2022] [Accepted: 08/23/2022] [Indexed: 05/27/2023]
Abstract
Multi-sensor fusion is important in the field of autonomous driving. A basic prerequisite for multi-sensor fusion is calibration between sensors. Such calibrations must be accurate and need to be performed online. Traditional calibration methods have strict rules. In contrast, the latest online calibration methods based on convolutional neural networks (CNNs) have gone beyond the limits of the conventional methods. We propose a novel algorithm for online self-calibration between sensors using voxels and three-dimensional (3D) convolution kernels. The proposed approach has the following features: (1) it is intended for calibration between sensors that measure 3D space; (2) the proposed network is capable of end-to-end learning; (3) the input 3D point cloud is converted to voxel information; (4) it uses five networks that process voxel information, and it improves calibration accuracy through iterative refinement of the output of the five networks and temporal filtering. We use the KITTI and Oxford datasets to evaluate the calibration performance of the proposed method. The proposed method achieves a rotation error of less than 0.1° and a translation error of less than 1 cm on both the KITTI and Oxford datasets.
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Zhai Y, Zhang S. A Novel LiDAR–IMU–Odometer Coupling Framework for Two-Wheeled Inverted Pendulum (TWIP) Robot Localization and Mapping with Nonholonomic Constraint Factors. SENSORS 2022; 22:s22134778. [PMID: 35808273 PMCID: PMC9268906 DOI: 10.3390/s22134778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 06/14/2022] [Accepted: 06/18/2022] [Indexed: 11/16/2022]
Abstract
This paper proposes a method to solve the problem of localization and mapping of a two-wheeled inverted pendulum (TWIP) robot on approximately flat ground using a Lidar–IMU–Odometer system. When TWIP is in motion, it is constrained by the ground and suffers from motion disturbances caused by rough terrain or motion shaking. Combining the motion characteristics of TWIP, this paper proposes a framework for localization consisting of a Lidar-IMU-Odometer system. This system formulates a factor graph with five types of factors, thereby coupling relative and absolute measurements from different sensors (including ground constraints) into the system. Moreover, we analyze the constraint dimension of each factor according to the motion characteristics of TWIP and propose a new nonholonomic constraint factor for the odometry pre-integration constraint and ground constraint factor in order to add them naturally to the factor graph with the robot state node on SE(3). Meanwhile, we calculate the uncertainty of each constraint. Utilizing such a nonholonomic constraint factor, a complete lidar–IMU–odometry-based motion estimation system for TWIP is developed via smoothing and mapping. Indoor and outdoor experiments show that our method has better accuracy for two-wheeled inverted pendulum robots.
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Lindqvist B, Kanellakis C, Mansouri SS, Agha-mohammadi AA, Nikolakopoulos G. COMPRA: A COMPact Reactive Autonomy Framework for Subterranean MAV Based Search-And-Rescue Operations. J INTELL ROBOT SYST 2022. [DOI: 10.1007/s10846-022-01665-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
AbstractThis work establishes COMPRA, a compact and reactive autonomy framework for fast deployment of Micro Aerial Vehicles (MAVs) in subterranean Search-and- Rescue (SAR) missions. A COMPRA-enabled MAV is able to autonomously explore previously unknown areas while specific mission criteria are considered e.g. an object of interest is identified and localized, the remaining useful battery life, the overall desired exploration mission duration. The proposed architecture follows a low-complexity algorithmic design to facilitate fully on-board computations, including nonlinear control, state-estimation, navigation, exploration behavior and object localization capabilities. The framework is mainly structured around a reactive local avoidance planner, based on enhanced Potential Field concepts and using instantaneous 3D pointclouds, as well as a computationally efficient heading regulation technique, based on depth images from an instantaneous camera stream. Those techniques decouple the collision-free path generation from the dependency of a global map and are capable of handling imprecise localization occasions. Field experimental verification of the overall architecture is performed in relevant unknown Global Positioning System (GPS)-denied environments.
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Chen K, Lopez BT, Agha-mohammadi AA, Mehta A. Direct LiDAR Odometry: Fast Localization With Dense Point Clouds. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3142739] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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High-Precision SLAM Based on the Tight Coupling of Dual Lidar Inertial Odometry for Multi-Scene Applications. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12030939] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Simultaneous Localization and Mapping (SLAM) is an essential feature in many applications of mobile vehicles. To solve the problem of poor positioning accuracy, single use of mapping scene, and unclear structural characteristics in indoor and outdoor SLAM, a new framework of tight coupling of dual lidar inertial odometry is proposed in this paper. Firstly, through external calibration and an adaptive timestamp synchronization algorithm, the horizontal and vertical lidar data are fused, which compensates for the narrow vertical field of view (FOV) of the lidar and makes the characteristics of vertical direction more complete in the mapping process. Secondly, the dual lidar data is tightly coupled with an Inertial Measurement Unit (IMU) to eliminate the motion distortion of the dual lidar odometry. Then, the value of the lidar odometry after correcting distortion and the pre-integrated value of IMU are used as constraints to establish a non-linear least-squares objective function. Joint optimization is then performed to obtain the best value of the IMU state values, which will be used to predict the state of IMU at the next time step. Finally, experimental results are presented to verify the effectiveness of the proposed method.
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Reinke A, Palieri M, Morrell B, Chang Y, Ebadi K, Carlone L, Agha-Mohammadi AA. LOCUS 2.0: Robust and Computationally Efficient Lidar Odometry for Real-Time 3D Mapping. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3181357] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Andrzej Reinke
- NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - Matteo Palieri
- NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - Benjamin Morrell
- NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - Yun Chang
- Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kamak Ebadi
- NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - Luca Carlone
- Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA
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Petracek P, Kratky V, Petrlik M, Baca T, Kratochvil R, Saska M. Large-Scale Exploration of Cave Environments by Unmanned Aerial Vehicles. IEEE Robot Autom Lett 2021. [DOI: 10.1109/lra.2021.3098304] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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12
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Ginting MF, Otsu K, Edlund JA, Gao J, Agha-Mohammadi AA. CHORD: Distributed Data-Sharing via Hybrid ROS 1 and 2 for Multi-Robot Exploration of Large-Scale Complex Environments. IEEE Robot Autom Lett 2021. [DOI: 10.1109/lra.2021.3061393] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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13
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Nguyen TM, Yuan S, Cao M, Lyu Y, Nguyen TH, Xie L. MILIOM: Tightly Coupled Multi-Input Lidar-Inertia Odometry and Mapping. IEEE Robot Autom Lett 2021. [DOI: 10.1109/lra.2021.3080633] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Fakoorian S, Santamaria-Navarro A, Lopez BT, Simon D, Agha-mohammadi AA. Towards Robust State Estimation by Boosting the Maximum Correntropy Criterion Kalman Filter With Adaptive Behaviors. IEEE Robot Autom Lett 2021. [DOI: 10.1109/lra.2021.3073646] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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DARE-SLAM: Degeneracy-Aware and Resilient Loop Closing in Perceptually-Degraded Environments. J INTELL ROBOT SYST 2021. [DOI: 10.1007/s10846-021-01362-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Palieri M, Morrell B, Thakur A, Ebadi K, Nash J, Chatterjee A, Kanellakis C, Carlone L, Guaragnella C, Agha-mohammadi AA. Corrections to “LOCUS: A Multi-Sensor Lidar-Centric Solution for High-Precision Odometry and 3D Mapping in Real-Time” [Apr 21 421-428]. IEEE Robot Autom Lett 2021. [DOI: 10.1109/lra.2021.3062780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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