Robust GICP-Based 3D LiDAR SLAM for Underground Mining Environment

Ren, Zhuli; Wang, Liguan; Bi, Lin

doi:10.3390/s19132915

Cited by 79 publications

(48 citation statements)

References 34 publications

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“…Robotics seems to be the main beneficent of the rapid development of 3D LiDARs [28][29][30] and this is in accordance with initial observations on the significant role that LiDAR-based systems are going to play for an efficient exploration of the surrounding environment, to allow human functionalities to be substituted by automatic or semiautomatic systems. Other examples of important applications for 3D LiDAR-based systems can be found in [13,[31][32][33], where 3D LiDAR data are used in combinations with cameras and radars for the indoor exploration of inside tunnels, underground mines, and caves.…”

Section: The Lidar Systemmentioning

confidence: 99%

LiDAR-Based System and Optical VHR Data for Building Detection and Mapping

Ullo

Zarro

Wojtowicz

et al. 2020

Sensors

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The aim of this paper is to highlight how the employment of Light Detection and Ranging (LiDAR) technique can enhance greatly the performance and reliability of many monitoring systems applied to the Earth Observation (EO) and Environmental Monitoring. A short presentation of LiDAR systems, underlying their peculiarities, is first given. References to some review papers are highlighted, as they can be regarded as useful guidelines for researchers interested in using LiDARs. Two case studies are then presented and discussed, based on the use of 2D and 3D LiDAR data. Some considerations are done on the performance achieved through the use of LiDAR data combined with data from other sources. The case studies show how the LiDAR-based systems, combined with optical Very High Resolution (VHR) data, succeed in improving the analysis and monitoring of specific areas of interest, specifically how LiDAR data help in exploring external environment and extracting building features from urban areas. Moreover the discussed Case Studies demonstrate that the use of the LiDAR data, even with a low density of points, allows the development of an automatic procedure for accurate building features extraction, through object-oriented classification techniques, therefore by underlying the importance that even simple LiDAR-based systems play in EO and Environmental Monitoring.

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Section: The Lidar Systemmentioning

confidence: 99%

LiDAR-Based System and Optical VHR Data for Building Detection and Mapping

Ullo

Zarro

Wojtowicz

et al. 2020

Sensors

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“…Also, sparse point clouds and high-speed moving platforms introducing motion distortion can affect the performance of this algorithm negatively [36]. Many improvements have been proposed [37,38] to mitigate the limitation and to improve the computation efficiency and accuracy of the ICP algorithm [39].…”

Section: Introductionmentioning

confidence: 99%

High Definition 3D Map Creation Using GNSS/IMU/LiDAR Sensor Integration to Support Autonomous Vehicle Navigation

İlçi

Toth

2020

Sensors

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Recent developments in sensor technologies such as Global Navigation Satellite Systems (GNSS), Inertial Measurement Unit (IMU), Light Detection and Ranging (LiDAR), radar, and camera have led to emerging state-of-the-art autonomous systems, such as driverless vehicles or UAS (Unmanned Airborne Systems) swarms. These technologies necessitate the use of accurate object space information about the physical environment around the platform. This information can be generally provided by the suitable selection of the sensors, including sensor types and capabilities, the number of sensors, and their spatial arrangement. Since all these sensor technologies have different error sources and characteristics, rigorous sensor modeling is needed to eliminate/mitigate errors to obtain an accurate, reliable, and robust integrated solution. Mobile mapping systems are very similar to autonomous vehicles in terms of being able to reconstruct the environment around the platforms. However, they differ a lot in operations and objectives. Mobile mapping vehicles use professional grade sensors, such as geodetic grade GNSS, tactical grade IMU, mobile LiDAR, and metric cameras, and the solution is created in post-processing. In contrast, autonomous vehicles use simple/inexpensive sensors, require real-time operations, and are primarily interested in identifying and tracking moving objects. In this study, the main objective was to assess the performance potential of autonomous vehicle sensor systems to obtain high-definition maps based on only using Velodyne sensor data for creating accurate point clouds. In other words, no other sensor data were considered in this investigation. The results have confirmed that cm-level accuracy can be achieved.

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“…The package loss means that some points are missing in the space caused by network overload or unstable connection [11]. Points drift means that the points representing the same stationary object have different coordinates at different frames [12]. Previous research often directly deployed density-based algorithms under these situations to filter the background and detect objects; however, the existing density-based method may misidentify moving objects as background points (irrelevant points).…”

Section: Introductionmentioning

confidence: 99%

Background Filtering and Object Detection With a Stationary LiDAR Using a Layer-Based Method

Song

Zhang²,

Liu³

et al. 2020

IEEE Access

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The connected vehicle environment is significant for the future road network. For constructing the connected vehicle environment, real-time data acquirement is always the prerequisite. Recently, using Light Detection and Ranging (LiDAR)-based roadside infrastructures are becoming a prevalent method of obtaining real-time traffic data. However, the collected raw data from LiDAR cannot usually be used directly. The steps of data processing, like background filtering and object detection, are necessary. The processed data can then be employed in different applications. This paper proposed a novel layer-based searching method that is established with the help of the point distribution features to distinguish moving objects from the point cloud. It aimed to address the unexpected influence of factors such as congested situations and package loss. The new approach was also evaluated compared with the state-of-the-art methods by applying field data. The results showed that the proposed method is more effective than other methods. This method may be applicable to other types of rotating LiDAR for improving the background filtering performance.

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Robust GICP-Based 3D LiDAR SLAM for Underground Mining Environment

Cited by 79 publications

References 34 publications

LiDAR-Based System and Optical VHR Data for Building Detection and Mapping

LiDAR-Based System and Optical VHR Data for Building Detection and Mapping

High Definition 3D Map Creation Using GNSS/IMU/LiDAR Sensor Integration to Support Autonomous Vehicle Navigation

Background Filtering and Object Detection With a Stationary LiDAR Using a Layer-Based Method

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