Numerical terramechanics simulation and validation of soil volume in wheel loader bucket

Stationary Light Detection and Ranging (LiDAR) systems provide detailed three-dimensional (3D) point clouds for pavement surface geometry measurements. Despite their long presence in the market, LiDAR systems are still relatively new to pavement engineers and there are only few studies verifying their accuracy. To bridge this gap, laboratory and field tests were performed to investigate the accuracy of LiDAR instrument. In the laboratory test, a wooden board was set up and a dial indicator was used to measure deflections when loads were applied. The magnitudes were compared with the deflections from LiDAR scans estimated by a low-order polynomial fit model and a thin-plate spline (TPS) fit model. In the field test, profiles were acquired for concrete pavement using a standard profilometer and the LiDAR system. Agreement among these profiles was investigated. Test results assured that this stationary LiDAR system could provide acceptable accuracy and precision for slab surface geometry measurements.

Dynamically Adjustable LiDAR with SPAD Array and Scanner

Potential and limitations of LiDAR altimetry in archaeological survey. Copper Age and Bronze Age settlements in southern Iberia

3D Modeling Approach of Building Construction based on Point Cloud Data Using LiDAR Byeongjun Oh, Minju Kim, Chanwoo Lee, Hunhee Cho and Kyung-In Kang Pages 906-912 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: LiDAR (Light Detection and Ranging) emerges as a mapping technology that provides fast, accurate, and reliable data with geometric representation of construction facilities. The technology has received high recognition especially since the importance of digital transformation in construction projects has been emphasized. However, the current state of 3D technologies including LiDAR encounters difficulty in recognizing and extracting accurate information on the as-built status of the buildings and construction sites. As a preliminary study for the development of a 3D architectural geometric representation of building and environment, this paper proposes a 3D modeling approach based on point cloud data obtained by LiDAR technology. This paper suggests a novel method for data acquisition on as-built status of construction sites and buildings during construction-operation phases. Furthermore, a field test has been performed for visualizing and modeling of the indoors of a residential house. The results of this paper are expected to inaugurate adaptation of LiDAR technology during the as-built process, and further implementation of digital technology after construction phase in construction projects. Keywords: LiDAR; 3D modeling; building construction; digital transformation; point cloud data DOI: https://doi.org/10.22260/ISARC2019/0121 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley

Light Detection and Ranging (LiDAR) sensors are critical components of the perception system and play a significant role in enabling fully autonomous driving. Given that LiDARs have a higher failure rate than other sensors, such as camera and radar, it is crucial to monitor the health of this component to increasing the availability of autonomous driving features. Such a health monitoring system can additionally provide cost-effective maintenance for retail and fleet, improve the service experience of retail customers, and ensure the fidelity of the data produced by the LiDAR for engineering development. Since LiDAR is a relatively new technology, there is currently limited work in the area of LiDAR health monitoring. The failure modes and degradation behavior of these components have not been thoroughly studied in the literature for automotive applications. Therefore, this paper reviews LiDAR external and internal failure modes and their impacts on the perception performance. The external failure modes are categorized into multiple fault classes such as sensor blockage due to a layer of debris on the sensor, mechanical damage to the sensor cover, and mounting issues. The internal faults corresponding to LiDAR subcomponents such as transmitter, receiver or scanning mechanism, are explored for these LiDAR types: mechanical spinning, flash LiDAR, Micro-opto-electromechanical mirror LiDAR, and micromotion technology LiDAR. The failure modes of each subcomponent are also investigated to determine if they can be categorized as slow degradation or sudden failure. It is concluded that mechanical spinning LiDARs are expected to have higher failure rates than solid-state LiDARs. Both internal and external LiDAR failure modes can lead to reduced accuracy and reliability in detecting objects and obstacles, compromising the safety of autonomous driving systems, and increasing the possibility of collisions.

Light Detection and Ranging (LiDAR) sensors are popular in Simultaneous Localization and Mapping (SLAM) owing to their capability of obtaining ranging information actively. Researchers have attempted to use the intensity information that accompanies each range measurement to enhance LiDAR SLAM positioning accuracy. However, before employing LiDAR intensities in SLAM, a calibration operation is usually carried out so that the intensity is independent of the incident angle and range. The range is determined from the laser beam transmitting time. Therefore, the key to using LiDAR intensities in SLAM is to obtain the incident angle between the laser beam and target surface. In a complex environment, it is difficult to obtain the incident angle robustly. This procedure also complicates the data processing in SLAM and as a result, further application of the LiDAR intensity in SLAM is hampered. Motivated by this problem, in the present study, we propose a Hyperspectral LiDAR (HSL)-based-intensity calibration-free method to aid point cloud matching in SLAM. HSL employed in this study can obtain an eight-channel range accompanied by corresponding intensity measurements. Owing to the design of the laser, the eight-channel range and intensity were collected with the same incident angle and range. According to the laser beam radiation model, the ratio values between two randomly selected channels’ intensities at an identical target are independent of the range information and incident angle. To test the proposed method, the HSL was employed to scan a wall with different coloured papers pasted on it (white, red, yellow, pink, and green) at four distinct positions along a corridor (with an interval of 60 cm in between two consecutive positions). Then, a ratio value vector was constructed for each scan. The ratio value vectors between consecutive laser scans were employed to match the point cloud. A classic Iterative Closest Point (ICP) algorithm was employed to estimate the HSL motion using the range information from the matched point clouds. According to the test results, we found that pink and green papers were distinctive at 650, 690, and 720 nm. A ratio value vector was constructed using 650-nm spectral information against the reference channel. Furthermore, compared with the classic ICP using range information only, the proposed method that matched ratio value vectors presented an improved performance in heading angle estimation. For the best case in the field test, the proposed method enhanced the heading angle estimation by 72%, and showed an average 25.5% improvement in a featureless spatial testing environment. The results of the primary test indicated that the proposed method has the potential to aid point cloud matching in typical SLAM of real scenarios.

The primary purpose of this study is to develop a foot pulse electrical circuit that can be integrated into a LIDAR system used for measuring track speed and curvature. LIght Detection And Ranging (LIDAR) technology is used in a wide variety of applications because it is capable of reliably producing accurate and precise measurements. While application of LIDAR technology is vast, this particular study focuses on its ability to accurately measure velocity and track geometry of rail tracks. A research team at Virginia Tech (VT) has already developed, tested, and proven the capability of LIDAR technology to be used for railway applications [1,2]. Their analysis shows that a railcar-mounted LIDAR system can accurately measure track geometry, centerline velocity, car body dynamics, and several other useful parameters. While this system is reliable and multifunctional, the prototype used for testing is not easily upgraded to include additional features without augmenting the software currently used to analyze and record the LIDAR signal. However, the prototype LIDAR system lacks several capabilities that are desirable for integrating the system with typical commercial systems on trains. One signal that commercial train systems typically have, which the LIDAR prototype does not have, is a foot pulse. The foot pulse is usually generated by a tachometer on the wheel of the train and aims to send out a pulse every time the train has travelled a foot. This signal is used for multiple other systems on the train, so in order to simplify integration of the developed LIDAR prototype into commercial train systems, the prototype was upgraded to include additional features. Other than the foot pulse, the upgrade also included acceleration detection, direction indication, and laser-enable signals to have a more complete prototype. The upgrade was executed using an external microcontroller and accelerometer to provide proof of concept while leaving the current LIDAR prototype’s software (and already proven capabilities) untouched. This paper focuses on using the information generated by the current LIDAR system to implement the additional features in an inexpensive, reliable, and easily retrofittable manner.

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.

Crop 3D modeling allows site-specific management at different crop stages. In recent years, light detection and ranging (LiDAR) sensors have been widely used for gathering information about plant architecture to extract biophysical parameters for decision-making programs. The study reconstructed vineyard crops using light detection and ranging (LiDAR) technology. Its accuracy and performance were assessed for vineyard crop characterization using distance measurements, aiming to obtain a 3D reconstruction. A LiDAR sensor was installed on-board a mobile platform equipped with an RTK-GNSS receiver for crop 2D scanning. The LiDAR system consisted of a 2D time-of-flight sensor, a gimbal connecting the device to the structure, and an RTK-GPS to record the sensor data position. The LiDAR sensor was facing downwards installed on-board an electric platform. It scans in planes perpendicular to the travel direction. Measurements of distance between the LiDAR and the vineyards had a high spatial resolution, providing high-density 3D point clouds. The 3D point cloud was obtained containing all the points where the laser beam impacted. The fusion of LiDAR impacts and the positions of each associated to the RTK-GPS allowed the creation of the 3D structure. Although point clouds were already filtered, discarding points out of the study area, the branch volume cannot be directly calculated, since it turns into a 3D solid cluster that encloses a volume. To obtain the 3D object surface, and therefore to be able to calculate the volume enclosed by this surface, a suitable alpha shape was generated as an outline that envelops the outer points of the point cloud. The 3D scenes were obtained during the winter season when only branches were present and defoliated. The models were used to extract information related to height and branch volume. These models might be used for automatic pruning or relating this parameter to evaluate the future yield at each location. The 3D map was correlated with ground truth, which was manually determined, pruning the remaining weight. The number of scans by LiDAR influenced the relationship with the actual biomass measurements and had a significant effect on the treatments. A positive linear fit was obtained for the comparison between actual dry biomass and LiDAR volume. The influence of individual treatments was of low significance. The results showed strong correlations with actual values of biomass and volume with R2 = 0.75, and when comparing LiDAR scans with weight, the R2 rose up to 0.85. The obtained values show that this LiDAR technique is also valid for branch reconstruction with great advantages over other types of non-contact ranging sensors, regarding a high sampling resolution and high sampling rates. Even narrow branches were properly detected, which demonstrates the accuracy of the system working on difficult scenarios such as defoliated crops.

Fusion of airborne LiDAR data and hyperspectral imagery for aboveground and belowground forest biomass estimation

Light detection and ranging (LiDAR) is a widely used technology for the acquisition of three-dimensional (3D) information about a wide variety of physical objects and environments. However, before conducting a campaign, a test is typically conducted to assess the potential of the utilized algorithm for information retrieval. It might not be a real campaign but rather a simulation to save time and costs. Here, a multi-platform LiDAR simulation model considering the location, direction, and wavelength of each emitted laser pulse was developed based on the large-scale remote sensing (RS) data and image simulation framework (LESS) model, which is a 3D radiative transfer model for simulating passive optical remote sensing signals using the ray tracing algorithm. The LESS LiDAR simulator took footprint size, returned energy, multiple scattering, and multispectrum LiDAR into account. The waveform and point similarity were assessed with the LiDAR module of the discrete anisotropic radiative transfer (DART) model. Abstract and realistic scenes were designed to assess the simulated LiDAR waveforms and point clouds. A waveform comparison in the abstract scene with the DART LiDAR module showed that the relative error was lower than 1%. In the realistic scene, airborne and terrestrial laser scanning were simulated by LESS and DART LiDAR modules. Their coefficients of determination ranged from 0.9108 to 0.9984. Their mean was 0.9698. The number of discrete returns fitted well and the coefficient of determination was 0.9986. A terrestrial point cloud comparison in the realistic scene showed that the coefficient of determination between the two sets of data could reach 0.9849. The performance of the LESS LiDAR simulator was also compared with the DART LiDAR module and HELIOS++. The results showed that the LESS LiDAR simulator is over three times faster than the DART LiDAR module and HELIOS++ when simulating terrestrial point clouds in a realistic scene. The proposed LiDAR simulator offers two modes for simulating point clouds: single-ray and multi-ray modes. The findings demonstrate that utilizing a single-ray simulation approach can significantly reduce the simulation time, by over 28 times, without substantially affecting the overall point number or ground pointswhen compared to employing multiple rays for simulations. This new LESS model integrating a LiDAR simulator has great potential in terms of simultaneously simulating LiDAR data and optical images based on the same 3D scene and parameters. As a proof of concept, the normalized difference vegetation index (NDVI) results from multispectral images and the vertical profiles from multispectral LiDAR waveforms were simulated and analyzed. The results showed that the proposed LESS LiDAR simulator can fulfill its design goals.

This paper describes an object detection and classification method for an Unmanned Ground Vehicle (UGV) using a range sensor and an image sensor. The range sensor and the image sensor are a 3D Light Detection And Ranging (LIDAR) sensor and a monocular camera, respectively. For safe driving of the UGV, pedestrians and cars should be detected on their moving routes of the vehicle. An object detection and classification techniques based on only a camera has an inherent problem. On the view point of detection with a camera, a certain algorithm should extract features and compare them with full input image data. The input image has a lot of information as object and environment. It is hard to make a decision of the classification. The image should have only one reliable object information to solve the problem. In this paper, we introduce a developed 3D LIDAR sensor and apply a fusion method both 3D LIDAR data and camera data. We describe a 3D LIDAR sensor which is developed by LG Innotek Consortium in Korea, named KIDAR-B25. The 3D LIDAR sensor detects objects, determines the object's Region of Interest (ROI) based on 3D information and sends it into a camera region for classification. In the 3D LIDAR domain, we recognize breakpoints using Kalman filter and then make a cluster using a line segment method to determine an object's ROI. In the image domain, we extract the object's feature data from the ROI region using a Haar-like feature method. Finally it is classified as a pedestrian or car using a trained database with an Adaboost algorithm. To verify our system, we make an experiment on the performance of our system which is mounted on a ground vehicle, through field tests in an urban area.

본 논문에서는 야지 환경에서 동작하는 무인이동로봇에서 획득한 3차원 LIDAR (Light Detection and Ranging) 센서 정보와 로봇이 이동하는 지형의 3차원 DSM (Digital Surface Map)에서 정사윤곽선(Ortho-edge) 특징영상을 생성하고 정합하여 로봇의 현재 위치를 추정하는 기술을 제안한다. 최근의 무인이동로봇의 위치 인식에 대한연구는 GPS (Global Positioning System), IMU (Inertial Measurement Unit), LIDAR 등의 위치인식 센서를 융합하는 경우가 많아지고 있다. 특히 LIDAR에서 획득한 거리정보를 ICP(Iterative Closest Point) 기반의 기하정합으로 로봇의 위치를 추정하는 기술이 개발되고 있다. 그러나 이동로봇에서 획득한 센서 정보는 DSM의 센싱 방향과 큰 차이차이가 있어 기존의 기하정합 기술을 사용하는데 어려움이 있다. 본 논문에서는 서로 다른 센싱 방향에서 획득한 3차원 LIDAR 거리정보와 DSM에서 정사윤곽선이라는 특징 영상을 생성하고 이들을 정합하여 로봇의 위치를 추정하는 새로운 기술을 제안한다. DSM으로부터 현재 시점의 정사윤곽선 영상을 생성하는 방법, 전방향 LIDAR 거리센서에서 정사윤곽선 영상을 생성하는 방법, 그리고 정사윤곽선 영상의 정합 기술을 설명하였다. 실험에서는 다양한 주행 경로에 대한 위치 추정의 오차를 분석하고 제안 기술의 성능의 우수성을 보였다. This paper presents a new localization technique of an UGV(Unmanned Ground Vehicle) by matching ortho-edge images generated from a DSM (Digital Surface Map) which represents the 3D geometric information of an outdoor navigation environment and 3D range data which is obtained from a LIDAR (Light Detection and Ranging) sensor mounted at the UGV. Recent UGV localization techniques mostly try to combine positioning sensors such as GPS (Global Positioning System), IMU (Inertial Measurement Unit), and LIDAR. Especially, ICP (Iterative Closest Point)-based geometric registration techniques have been developed for UGV localization. However, the ICP-based geometric registration techniques are subject to fail to register 3D range data between LIDAR and DSM because the sensing directions of the two data are too different. In this paper, we introduce and match ortho-edge images between two different sensor data, 3D LIDAR and DSM, for the localization of the UGV. Details of new techniques to generating and matching ortho-edge images between LIDAR and DSM are presented which are followed by experimental results from four different navigation paths. The performance of the proposed technique is compared to a conventional ICP-based technique.

Key messageNew technologies can take us towards real precision forestry: the terrestrial single-photon avalanche diode (SPAD) light detection and ranging (LiDAR) has a great potential to outperform conventional linear mode LiDARs in measuring tree parameters at the stand level.ContextPrecision forestry together with new sensor technologies implies Digital Forest Inventories for estimation of volume and quality of trees in a stand.AimsThis study compared commercial LiDAR, new prototype SPAD LiDAR, and manual methods for measuring tree quality attributes, i.e., diameter at breast height (DBH) and trunk curvature in the forest stand.MethodsWe measured 7 Scots pine trees (Pinus sylvestris) with commercial LiDAR (Zeb Horizon by GeoSLAM), prototype SPAD LiDAR, and manual devices. We compared manual measurements to the DBH and curvature values estimated based on LiDAR data. We also scanned a densely branched Picea abies to compare penetrability of the LiDARs and detectability of the obstructed trunk.ResultsThe DBH values deviated 1–3 cm correlating to the specified accuracies of the employed devices, showing close to acceptable results. The curvature values deviated 1–6 cm implying distorted range measurements from the top part of the trunks and inaccurate manual measurement method, leaving space for improvement. The most important finding was that the SPAD LiDAR outperformed conventional LiDAR in detecting tree stem of the densely branched spruce.ConclusionThese results represent preliminary but clear evidence that LiDAR technologies are already close to acceptable level in DBH measurements, but not yet satisfactory for curvature measurements. In addition, terrestrial SPAD LiDAR has a great potential to outperform conventional LiDARs in forest measurements of densely branched trees.

Simulation of satellite, airborne and terrestrial LiDAR with DART (I): Waveform simulation with quasi-Monte Carlo ray tracing