CONTINUOUS MAPPING OF TUNNEL WALLS IN A GNSS-DENIED ENVIRONMENT
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Abstract. The need for reliable systems for capturing precise detail in tunnels has increased as the number of tunnels (e.g., for cars and trucks, trains, subways, mining and other infrastructure) has increased and the age of these structures and, subsequent, deterioration has introduced structural degradations and eventual failures. Due to the hostile environments encountered in tunnels, mobile mapping systems are plagued with various problems such as loss of GNSS signals, drift of inertial measurements systems, low lighting conditions, dust and poor surface textures for feature identification and extraction. A tunnel mapping system using alternate sensors and algorithms that can deliver precise coordinates and feature attributes from surfaces along the entire tunnel path is presented. This system employs image bridging or visual odometry to estimate precise sensor positions and orientations. The fundamental concept is the use of image sequences to geometrically extend the control information in the absence of absolute positioning data sources. This is a non-trivial problem due to changes in scale, perceived resolution, image contrast and lack of salient features. The sensors employed include forward-looking high resolution digital frame cameras coupled with auxiliary light sources. In addition, a high frequency lidar system and a thermal imager are included to offer three dimensional point clouds of the tunnel walls along with thermal images for moisture detection. The mobile mapping system is equipped with an array of 16 cameras and light sources to capture the tunnel walls. Continuous images are produced using a semi-automated mosaicking process. Results of preliminary experimentation are presented to demonstrate the effectiveness of the system for the generation of seamless precise tunnel maps.Keywords:
Mobile mapping
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Abstract. Nowadays, portable Mobile Mapping Systems (MMSs) and robotic mapping platforms leveraging on Simultaneous Localization and Mapping (SLAM) methods are gaining increasing attention for architectural and construction surveying, representing an efficient solution for geometric data acquisition for scan-to-BIM purposes. However, the applicability of standard modelling workflows and the accuracy of Building Information Models (BIM) that can be obtained from SLAM-based point clouds is still an open question. In this paper, we propose a preliminary evaluation on the feasibility of extracting as-built BIM from (i) a point cloud acquired with a commercial portable MMS, and (ii) a point cloud obtained through an open-source SLAM algorithm, surveying the environment with an autonomous mobile robotic platform. In both cases, the main structural elements of the test site are accurately generated, thus showing promising results. On the other hand, the experiment highlights also the need for SLAM systems capable of providing less noisy point clouds, in order to capture and model architectural details.
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This paper presents a “3D Building Scene Reconstruction Based on LiDAR Point Cloud.” The 3D Light Detection and Ranging (LiDAR) can take the stereo information under the environment. The short distance research is growing recently for 3D LiDAR. We use the point cloud data of interior building to perform 3D model.
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Mobile mapping systems (MMSs) provide capability for direct geo-referencing, and rapid and convenient acquisition of geographic data elements. Combining global positioning system/inertial measurement unit/distance measurement indicator (GPS/IMU/DMI) data is one of the most attractive methodologies for such systems. Inertial measurement unit data give attitude information, but the equipment is expensive and faces technological blockade by foreign companies. To overcome this limitation, the current paper explores a soft-IMU technique based on panorama vision, which can acquire more information from the environment because of its wide instantaneous field of view (IFOV) compared with traditional cameras. The proposed soft-IMU technology automatically extracts and tracks image registration points (IRPs) in panorama data, and corrects paths using correction points. The development history of the mobile mapping system, the software and hardware configuration of our prototype digital panorama mapping DPM™ products, the system calibration model principle and object measurement are described. Finally, it is demonstrated that the system precision meets the demands of daily data acquisition in field experimental studies.
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Nowadays, attitude information is necessary in land, maritime, aerial, and space applications. This attitude information is commonly determined using two distinct approaches. The first uses expensive inertial sensors typically aided with GNSS technology, while the second, one or more high-end GNSS receivers connected to multiple GNSS antennas. Previous studies showed the possibility to match the estimated position and velocity from a Closely Coupled integration using a high grade Inertial Measurement Unit (IMU) with the ones from a Tightly Coupled (TC) integration using a low grade IMU, [1]. In these previous studies was also shown that the use of higher grade IMU always led to a superior quality of the estimated attitude. Consequently, when high quality attitude data are necessary and the cost is a sensitive issue, the low cost inertial sensors integrated with GNSS sensors alone are not the solution. The present paper proposes a solution for this limitation introduced by the use of low grade inertial sensors. The proposed solution is characterized by assisting a conventional TC integration using a low cost system with attitude previously estimated by a multi-antenna GNSS system. Two different GNSS attitude methods are described and evaluated. The selected method is used to successfully demonstrate the proposed concept, which is a very cost-effective solution for applications where high quality information is required.
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Abstract. With their high recording rate of hundreds of thousands of points acquired per second, speed of execution and a remote acquisition mode, SLAM based mobile mapping systems (MMS) are a very powerful solution for capturing 3D point clouds in real time, simply by walking in the area of interest. Regarding indoor surveys, these MMS have been integrated in handheld or backpack solutions and become fast scanning sensors. Despite their advantages, the geometric accuracy of 3D point clouds guaranteed with these sensors is lower than the one reachable with static TLS. In this paper the effectiveness of two recent mobile mapping systems namely the GeoSLAM ZEB-REVO RT and the more recent GreenValley LiBackPack C50 is investigated for indoor surveys. In order to perform a reliable assessment study, several datasets produced with each sensor are compared to the high-cost georeferenced point cloud obtained with static laser scanning target-based technique.
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This paper mainly studies how to collect useful point cloud data efficiently and accurately by 3D lidar and gives a set of effective fast 3D modeling method. Using LiDAR RS-LIDAR-16 to scan the roads and their surroundings at different measuring stations, the 3d laser point clouds of several stations were obtained. Point cloud registration and denoising were carried out to eliminate noisy scattered point clouds. The experimental results show that this method can realize the modeling of three-dimensional point clouds of different stations and has good modeling effect.
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Recently, as a result of developments in microelectromechanical systems (MEMS) technology, various studies have been conducted to perform positioning by combining low-cost MEMS-based IMUs and the GNSS. The advantage of MEMS IMU is its low cost; however, its limitation is that the navigation error rapidly increases when disconnected from the GNSS. Therefore, precise positioning is difficult in tunnels or urban environments, where GNSS signals are unreliable. For this reason, additional sensors are needed. In this study, we intend to improve the accuracy of existing GNSS/IMU couplings using internal sensors and a magnetometer (MAG) attached to a vehicle.In this study, a positioning algorithm is developed based on the extended Kalman filter using on-board vehicle sensors and a MAG in addition to GNSS/IMU. A wheel speed sensor (WSS) and yaw rate sensor (YRS) were used as the on-board vehicle sensors. Experimental data were acquired and performance was analyzed. The results show that the GNSS/MEMS-IMU/WSS/YRS/MAG combination has the most stable positional accuracy, with a horizontal deviation of about 3.6 m observed in 10 zones of 30-second GNSS signal blockage. The performance was not significantly improved by adding the YRS; however, when the WSS and the MAG were used, the performance was greatly improved in the zones with GNSS signal blockage.
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Abstract. In order to accomplish the automatic mobile mapping task in a small area of interest, a low cost UAV system is proposed in this paper. Multiple sensors including a global shutter camera and an inertial measurement unit are calibrated and synchronized to collect data from the area of interest. First the images are matched by the chronological order and the SfM is utilized. Then the origin SfM result is integrated with the IMU data by adding the IMU constraints into the bundle adjustment. At last the photogrammetry point clouds are generated using PMVS according to the extrinsic parameters. Experiments are undertaken in a typical scene with photogrammetry point clouds generated. The trajectory estimated by the proposed integration method are compared with the method that relies on image only, showing that the proposed method has better performance.
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Structure from Motion
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ION GNSS+ 2021 Student Paper Award Winner. Precise and continuous positioning and navigation in urban areas can be achieved using high performance and expensive GNSS and IMU technology. However, modern applications demand higher accuracy and precision using much lower-cost sensors. In this research, next-generation, low-cost multi-frequency GNSS, microelectromechanical (MEMS) based Inertial Measurement Unit (IMU), and a patch antenna was used to obtain decimetre level accuracy in a suburban and urban environment. To compensate for the low-cost GNSS hardware errors, Precise Point Positioning (PPP) augmentation, ionosphere constraining (IC) and the third frequency of GNSS were included while vehicle constraining/ Non- holonomic constraints (NHC) were used on MEMS IMU measurements. A unique combination of the low-cost hardware and software constraining was used to bridge the GNSS gaps in an urban environment to provide a continuous, accurate, and reliable position solution that is novel and has not been explored and published earlier. In the past, high-precision, dual-frequency (DF) GNSS PPP + IMU and low-cost, single-frequency (SF) GNSS PPP + IMU were examined and analysed in the PPP + IMU research area. The accuracy performance of the TF PPP + IMU with constraining algorithm was tested rigorously using various combinations of satellites such as individual constellations and a combination of constellations during the introduced GNSS outages. TF PPP and IMU demonstrates less than a decimetre-level accuracy in the presence of a sufficient number of satellites. During 30 seconds of introduced GNSS signal loss, the overall rms of TF GNSS PPP + IMU + IC algorithm is 10-40% better than DF GNSS PPP + IMU, as the number of satellites available reduces progressively from 4 to 1. The addition of IC and NHC helps in improving the accuracy of the positioning solution during reconvergence after an outage. The decimetre level accuracy results obtained during partial GNSS availability indicate a significant step forward in the low-cost navigation area for applications such as low-cost autonomous vehicles, intelligent transportation systems, UAVs., etc. that demand a decimetre level of accuracy in all environments.
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Mobile laser scanning (MLS) systems mainly comprise laser scanners and mobile mapping platforms. Typical MLS systems are able to acquire three-dimensional point clouds with 1-10 centimeter point spacing at a normal driving or walking speed in the street or indoor environments. The MLS' advantages of efficiency and stability make it a quite practical tool for three-dimensional urban modeling. This paper reviews the latest advances in 3D modeling of the LiDAR-based mobile mapping system (MMS) point cloud, including LiDAR Simultaneous Localization and Mapping (SLAM), point cloud registration, feature extraction, object extraction, semantic segmentation, and deep learning processing. Then typical urban modeling applications based on MMS are also discussed.
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