Scanning Lidar Research Articles

Sharp Performance Bounds for PASTA

LiDAR is a standard sensor choice for self-localization and SLAM on indoor autonomous robots. While there are many methods to estimate a robot’s location using LiDAR measurements, most rely on algorithms that solve a generic LiDAR scan matching problem. When safety is a concern, these algorithms must provide a bound on the localization error to enable safety enforcing controllers, such as those based on Control Barrier Functions. Unfortunately, most existing scan matching algorithms offer no formal guarantees and are tailored to structured, high-resolution 3D point clouds. In this paper, we present an improved theoretical analysis for a low-cost alternative to these methods named Pasta (Provably Accurate Simple Transformation Alignment), originally introduced in marchi2022lidar. We provide a formal worst-case guarantee on the localization error and show, experimentally, that it is tight. This characterization of the localization error simplifies the interface between high-dimensional perception data and safety-critical control.

Research on linear array scanning lidar and photon signal processing technology based on InGaAs single-photon detector

Research on linear array scanning lidar and photon signal processing technology based on InGaAs single-photon detector

Near-Field Signal Correction and Retrieval Technique for Mie Scattering Vertical Scanning Lidar

Near-Field Signal Correction and Retrieval Technique for Mie Scattering Vertical Scanning Lidar

Calculation of scanning lidar returns while sounding cirrus clouds with quasi-horizontally oriented crystals

Calculation of scanning lidar returns while sounding cirrus clouds with quasi-horizontally oriented crystals

Application of detector deviation from focal plane in high speed scanning LiDAR

Application of detector deviation from focal plane in high speed scanning LiDAR

Improving the sensitivity and bandwidth of time-of-flight scanning LIDAR using few-mode preamplified receivers

Improving the sensitivity and bandwidth of time-of-flight scanning LIDAR using few-mode preamplified receivers

A Phased Array Antenna System of a Millimeter-wave FMCW Radar for Blind Spot Detection of Mobile Robots

Mobile robots have been extensively used in manufacturing plants for inter-logistic transportation in recent years. This paper covers a phased array antenna design for a millimeter wave radar system to improve lidar-based navigation systems' safety and environmental consciousness. The K-band phased array antenna, when integrated with 24 GHz Frequency-Modulated-Continuous-Wave (FMCW) radar, not only enhances the accuracy of the 2-D Area Scanning lidar system but also helps with the safe operation of the vehicle. The safety improvement is made by covering blind spots to mitigate collision risks during the rotations. The paper first reviews the system-level details of the 2D lidar sensor and shows the blind spots when integrated into a Mobile Robot prototype. Then continues with the inclusion of an FMCW Low-Speed Ramp radar system and discusses the design details of the proposed K-band antenna array, which will be integrated with a radar sensor.

Accuracy of Rockfall Volume Reconstruction from Point Cloud Data—Evaluating the Influences of Data Quality and Filtering

Rockfall processes are now commonly studied through monitoring campaigns using repeat lidar scanning. Accordingly, several recent studies have evaluated how the temporal resolution of data collection and various data-processing decisions can influence the apparent rockfall volumes estimated using typical rockfall database creation workflows. However, there is a lack of studies that consider how data quality and associated data-processing decisions influence rockfall volume estimation. In this work, we perform a series of tests based on an existing reference rockfall database from the Front Range of Colorado, USA, to isolate the influences of data resolution (point spacing), individual point precision, and the filter threshold applied to change results, on the volume estimates obtained for rockfalls. While the effects of individual point precision were found to be limited for typical levels of gaussian noise (standard deviation per coordinate direction ≤ 0.02 m), data resolution and change filter threshold were found to have systematic impacts on volume estimates, with the volume estimates for the smallest rockfalls decreasing substantially with increases in point spacing and change filter threshold. Because these factors disproportionately impact volume estimates for smaller rockfalls, when these factors change, the slope of the apparent power law that describes the relative frequency-volume distribution of rockfalls changes. Evidence is presented that suggests that this phenomenon can explain discrepancies between power law slopes presented in the literature based on studies focused on different scales of rockfall activity. Overall, this study demonstrates the impacts of raw data attributes on rockfall volume estimation and presents an additional effect that tends to bias rockfall frequency–magnitude power law relationships towards underestimation of the relative prevalence of small rockfalls.

New Structural Complexity Metrics for Forests from Single Terrestrial Lidar Scans

We developed new measures of structural complexity using single point terrestrial laser scanning (TLS) point clouds. These metrics are depth, openness, and isovist. Depth is a three-dimensional, radial measure of the visible distance in all directions from plot center. Openness is the percent of scan pulses in the near-omnidirectional view without a return. Isovists are a measurement of the area visible from the scan location, a quantified measurement of the viewshed within the forest canopy. 243 scans were acquired in 27 forested stands in the Pacific Northwest region of the United States, in different ecoregions representing a broad gradient in structural complexity. All stands were designated natural areas with little to no human perturbations. We created “structural signatures” from depth and openness metrics that can be used to qualitatively visualize differences in forest structures and quantitively distinguish the structural composition of a forest at differing height strata. In most cases, the structural signatures of stands were effective at providing statistically significant metrics differentiating forests from various ecoregions and growth patterns. Isovists were less effective at differentiating between forested stands across multiple ecoregions, but they still quantify the ecological important metric of occlusion. These new metrics appear to capture the structural complexity of forests with a high level of precision and low observer bias and have great potential for quantifying structural change to forest ecosystems, quantifying effects of forest management activities, and describing habitat for organisms. Our measures of structure can be used to ground truth data obtained from aerial lidar to develop models estimating forest structure.

Clustering Denoising of 2D LiDAR Scanning in Indoor Environment Based on Keyframe Extraction

In the indoor laser simulation localization and mapping (SLAM) system, the signal emitted by the LiDAR sensor is easily affected by lights and objects with low reflectivity during the transmission process, resulting in more noise points in the laser scan. To solve the above problem, this paper proposes a clustering noise reduction method based on keyframe extraction. First, the dimension of a scan is reduced to a histogram, and the histogram is used to extract the keyframes. The scans that do not contain new environmental information are dropped. Secondly, the laser points in the keyframe are divided into different regions by the region segmentation method. Next, the points are separately clustered in different regions and it is attempted to merge the point sets from adjacent regions. This greatly reduces the dimension of clustering. Finally, the obtained clusters are filtered. The sets with the number of laser points lower than the threshold will be dropped as abnormal clusters. Different from the traditional clustering noise reduction method, the technique not only drops some unnecessary scans but also uses a region segmentation method to accelerate clustering. Therefore, it has better real-time performance and denoising effect. Experiments on the MIT dataset show that the method can improve the trajectory accuracy based on dropping a part of the scans and save a lot of time for the SLAM system. It is very friendly to mobile robots with limited computing resources.

Estimation of lidar-based gridded DEM uncertainty with varying terrain roughness and point density

Light detection and ranging (lidar) scanning systems can be used to provide a point cloud with high quality and point density. Gridded digital elevation models (DEMs) interpolated from laser scanning point clouds are widely used due to their convenience, however, DEM uncertainty is rarely provided. This paper proposes an end-to-end workflow to quantify the uncertainty (i.e., standard deviation) of a gridded lidar-derived DEM. A benefit of the proposed approach is that it does not require independent validation data measured by alternative means. The input point cloud requires per point uncertainty which is derived from lidar system observational uncertainty. The propagated uncertainty caused by interpolation is then derived by the general law of propagation of variances (GLOPOV) with simultaneous consideration of both horizontal and vertical point cloud uncertainties. Finally, the interpolated uncertainty is then scaled by point density and a measure of terrain roughness to arrive at the final gridded DEM uncertainty. The proposed approach is tested with two lidar datasets measured in Waikoloa, Hawaii, and Sitka, Alaska. Triangulated irregular network (TIN) interpolation is chosen as the representative gridding approach. The results indicate estimated terrain roughness/point density scale factors ranging between 1 (in flat areas) and 7.6 (in high roughness areas), with a mean value of 2.3 for the Waikoloa dataset and between 1 and 9.2 with a mean value of 1.2 for the Sitka dataset. As a result, the final gridded DEM uncertainties are estimated between 0.059 m and 0.677 m with a mean value of 0.164 m for the Waikoloa dataset and between 0.059 m and 1.723 m with a mean value of 0.097 m for the Sitka dataset.

A GNSS/INS/LiDAR Integration Scheme for UAV-Based Navigation in GNSS-Challenging Environments.

Unmanned aerial vehicle (UAV) navigation has recently been the focus of many studies. The most challenging aspect of UAV navigation is maintaining accurate and reliable pose estimation. In outdoor environments, global navigation satellite systems (GNSS) are typically used for UAV localization. However, relying solely on GNSS might pose safety risks in the event of receiver malfunction or antenna installation error. In this research, an unmanned aerial system (UAS) employing the Applanix APX15 GNSS/IMU board, a Velodyne Puck LiDAR sensor, and a Sony a7R II high-resolution camera was used to collect data for the purpose of developing a multi-sensor integration system. Unfortunately, due to a malfunctioning GNSS antenna, there were numerous prolonged GNSS signal outages. As a result, the GNSS/INS processing failed after obtaining an error that exceeded 25 km. To resolve this issue and to recover the precise trajectory of the UAV, a GNSS/INS/LiDAR integrated navigation system was developed. The LiDAR data were first processed using the optimized LOAM SLAM algorithm, which yielded the position and orientation estimates. Pix4D Mapper software was then used to process the camera images in the presence of ground control points (GCPs), which resulted in the precise camera positions and orientations that served as ground truth. All sensor data were timestamped by GPS, and all datasets were sampled at 10 Hz to match those of the LiDAR scans. Two case studies were considered, namely complete GNSS outage and assistance from GNSS PPP solution. In comparison to the complete GNSS outage, the results for the second case study were significantly improved. The improvement is described in terms of RMSE reductions of approximately 51% and 78% for the horizontal and vertical directions, respectively. Additionally, the RMSE of the roll and yaw angles was reduced by 13% and 30%, respectively. However, the RMSE of the pitch angle was increased by about 13%.

UAV LiDAR Metrics for Monitoring Crop Height, Biomass and Nitrogen Uptake: A Case Study on a Winter Wheat Field Trial

Efficient monitoring of crop traits such as biomass and nitrogen uptake is essential for an optimal application of nitrogen fertilisers. However, currently available remote sensing approaches suffer from technical shortcomings, such as poor area efficiency, long postprocessing requirements and the inability to capture ground and canopy from a single acquisition. To overcome such shortcomings, LiDAR scanners mounted on unmanned aerial vehicles (UAV LiDAR) represent a promising sensor technology. To test the potential of this technology for crop monitoring, we used a RIEGL Mini-VUX-1 LiDAR scanner mounted on a DJI Matrice 600 pro UAV to acquire a point cloud from a winter wheat field trial. To analyse the UAV-derived LiDAR point cloud, we adopted LiDAR metrics, widely used for monitoring forests based on LiDAR data acquisition approaches. Of the 57 investigated UAV LiDAR metrics, the 95th percentile of the height of normalised LiDAR points was strongly correlated with manually measured crop heights (R2 = 0.88) and with crop heights derived by monitoring using a UAV system with optical imaging (R2 = 0.92). In addition, we applied existing models that employ crop height to approximate dry biomass (DBM) and nitrogen uptake. Analysis of 18 destructively sampled areas further demonstrated the high potential of the UAV LiDAR metrics for estimating crop traits. We found that the bincentile 60 and the 90th percentile of the reflectance best revealed the relevant characteristics of the vertical structure of the winter wheat plants to be used as proxies for nitrogen uptake and DBM. We conclude that UAV LiDAR metrics provide relevant characteristics not only of the vertical structure of winter wheat plants, but also of crops in general and are, therefore, promising proxies for monitoring crop traits, with potential use in the context of Precision Agriculture.

Best practices to use the iPad Pro LiDAR for some procedures of data acquisition in the urban forest

The search for means to improve urban forest inventories is challenging for small communities and cities with a limited budget. Mobile applications on iPad or iPhone seem promising equipment to make some inventory practices cheaper and faster, although procedures of use are still limited. So, we tested the LiDAR scanner application on an iPad Pro 2020 for trunk perimeter measures and the position for 10 different groups of species in the Polish Airmen Park, Kraków (Poland). For each group, in 10 trees, we measured the perimeter at breast height (PBH) and relative tree trunk position. The first procedure tested the estimation of PBH according to the distance of an iPad Pro from the trunk, the time of 3D data acquisition and the number of turns around trees. The second procedure tested PBH estimations according to the number of trees scanned in just one try (3, 6 and 10 trees). The third procedure tested the estimation of the relative position of tree trunks from other trees. In each procedure, we compared 3D point clouds generated by an App running on iPad Pro with data from a measuring tape and FARO FOCUS 3D (TLS) point clouds. The results showed that the shorter the distance from the iPad Pro to the trunk surface, the more precise the PBH estimation, not significantly different (p > 0,01) from TLS values. Distances between 1.0 and 2.0 m would be most suitable for the iPad Pro application but performing two turns around the tree trunk would improve results for PBH measurements. Trees scanned as 3-tree groups with the iPad Pro presented the smallest PBH differences compared to TLS point clouds. No significant differences (p > 0,01) were found between the methods for estimating the distance among trees, which shows that the iPad Pro can deliver a precise relative position of tree trunks.

Automatic evaluation of rebar spacing and quality using LiDAR data: Field application for bridge structural assessment

This paper proposes a framework to automatically quantify the quality of rebar placement in the field to improve construction quality and long-term durability of the bridge structures. In this study, three-dimensional point clouds of a concrete bridge construction site were acquired using a mobile LiDAR scanner. Additionally, two algorithms, including projection algorithm and slicing algorithm, were developed to automatically extract the bridge rebar locations for layout quantification at the global level, bin level, and local level. Subsequently, LiDAR extracted rebar placement quality was compared to design drawings to investigate the effectiveness. This study also evaluated the quality of other concrete deck components, which include formwork panels and the contact surface between rebars and anchor bolts. The research revealed that the proposed framework can inform the rebar placement quality in the field prior to concrete pour, providing a permanent record of the bridge deck quality for future inspections and assessments.

Direct immersogeometric fluid flow and heat transfer analysis of objects represented by point clouds

Immersogeometric analysis (IMGA) is a geometrically flexible method that enables one to perform multiphysics analysis directly using complex computer-aided design (CAD) models. While the IMGA approach is well-studied and has a remarkable advantage over traditional CFD, IMGA still requires a well-defined B-rep model to represent the geometry. Obtaining such a model can sometimes be equally as challenging as creating a body-fitted mesh. To address this issue, we develop a novel IMGA approach for the simulation of incompressible and compressible flows around complex geometries represented by point clouds in this work. The point cloud representation of geometries is a direct method for digitally acquiring geometric information using LiDAR scanners, optical scanners, or other passive methods such as multi-view stereo images. The point cloud object’s geometry is represented using a set of unstructured points in the Euclidean space with (possible) orientation information in the form of surface normals. Due to the absence of topological information in the point cloud model, there are no guarantees for the geometric representation to be watertight or 2-manifold or to have consistent normals. To perform IMGA directly using point cloud geometries, we first develop a method for estimating the inside–outside information and the surface normals directly from the point cloud. We also propose a method to compute the Jacobian determinant for the surface integration (over the point cloud) necessary for the weak enforcement of Dirichlet boundary conditions. We validate these geometric estimation methods by comparing the geometric quantities computed from the point cloud with those obtained from analytical geometry and tessellated CAD models. In this work, we also develop thermal IMGA to simulate heat transfer in the presence of flow over complex geometries. The proposed framework is tested for a wide range of Reynolds and Mach numbers on benchmark problems of geometries represented by point clouds, showing the robustness and accuracy of the method. Finally, we demonstrate the applicability of our approach by performing IMGA on large industrial-scale construction machinery represented using a point cloud of more than 12 million points.

Measuring Cloud Base Height and Cloud Coverage using Elastic Multiangle Lidars at Pierre Auger Observatory

Cloud features above the Pierre Auger Observatory (Mendoza Province, Argentina) significantly affect the reconstruction of Extensive Air Showers. In this work, we present seasonal variations of cloud-base height, cloud coverage, and correlation between different sites using the information of elastic multiangle lidar data. This system locates the presence of clouds by measuring the spikes in the backscattered photons detected in the direction of the sweep performed during each lidar scan, outside the field of view (FOV) of the fluorescence detectors. Horizontal homogeneity should be assumed to translate these results to the full array. This ansatz is verified by a set of dedicated horizontal lidar shots performed for a few seconds every hour inside the FOV of the fluorescence detectors. Here we present the results for the period 2007 to 2016, using all the continuous lidar scans available in the lidar database. The analysis algorithm used for cloud retrieval has been upgraded and is based on a different concept than the previous one. How cloud parameters vary across seasons is investigated, and conclusions about cloud homogeneity across the Pierre Auger array are given.

Relative Pose Estimation of Non-Cooperative Space Targets Using a TOF Camera

It is difficult to determine the accurate pose of non-cooperative space targets in on-orbit servicing (OOS). The visual camera is easily affected by the extreme light environment in space, and the scanning lidar will have motion distortion when the target moves at high speed. Therefore, we proposed a non-cooperative target pose-estimation system combining a registration and a mapping algorithm using a TOF camera. We first introduce the projection model of the TOF camera and proposed a new calibration method. Then, we introduce the three modules of the proposed method: the TOF data preprocessing module, the registration module and the model mapping module. We assembled the experimental platform to conduct semi-physical experiments; the results showed that the proposed method has the smallest translation error 8 mm and Euler angle error 1° compared with other classical methods. The total time consumption is about 100 ms, and the pose tracking frequency can reach 10 Hz. We can conclude that the proposed pose-estimation scheme can achieve the high-precision pose estimation of non-cooperative targets and meet the requirements necessary for aerospace applications.

Calculation of the Signal of a Scanning Lidar for Remote Sensing of Cirrus Clouds Containing Predominantly Horizontally Oriented Crystals

Calculation of the Signal of a Scanning Lidar for Remote Sensing of Cirrus Clouds Containing Predominantly Horizontally Oriented Crystals

Forest digital twin as a relaxation environment: A pilot study

Forest environments have been proven beneficial for physiological well-being, supporting relaxation and meditative processes. Unfortunately, some groups, predominantly those with reduced mobility, are prevented from forest visitation. Presenting such environments in virtual reality could provide a viable substitute. However, as forest structure and composition are important aspects of its restorative power, to accurately compare the efficacy of virtual forests to that of real natural spaces, the virtual environment should match the real location as closely as possible. Furthermore, if participants achieve similar benefits in both settings, virtual copies (digital twins) of forests could be a viable option for studying forest bathing in a controlled environment. We collected LiDAR scans of a forest location near Prague, took spatial audio recordings of the forest ambiance, and built the forest’s digital twin in Unreal Engine. To compare the therapeutic efficacy of the virtual forest with its real counterpart, groups of volunteers spent half an hour in either the real forest, the virtual forest, or both. We collected participants’ demographic and psychometric data, assessing their relaxation, emotional state, and cybersickness before and after the session. Our data show an increase in relaxation with no significant differences between the environments, although participants’ emotional states did not improve in either condition. We found that participants’ experiences were comparable between the environments, but cybersickness limited the potential efficacy of virtual forest bathing. The limitations of the virtual forests as a platform for research into forest bathing are discussed.