Conventional Lidar Research Articles

An evaluation of consumer smartphones for generating bolus and surface mould applicators for radiation oncology.

The use of Computed Tomography (CT) imaging data to create 3D printable patient-specific devices for radiation oncology purposes is already well established in the literature and has shown to have superior conformity than conventional methods. Using non-ionizing radiation imaging techniques such as photogrammetry or laser scanners in-lieu of a CT scanner presents many desirable benefits including reduced imaging dose and fabrication of the device can be completed prior to simulation. With recent advancements in smartphone-based technology, photographic and LiDAR-based technologies are more readily available than ever before and to a high level of quality. As a result, these non-ionizing radiation imaging methods are now able to generate patient-specific devices that can be acceptable for clinical use. In this work, we aim to determine if smartphones can be used by radiation oncologists or other radiation oncology staff to generate bolus or brachytherapy surface moulds instead of conventional CT with equivalent or comparable accuracy. This work involved two separate studies: a phantom and participant study. For the phantom study, a RANDO anthropomorphic phantom (limited to the nose region) was used to generate 3D models based on three different imaging techniques: conventional CT, photogrammetry & LiDAR which were both acquired on a smartphone. Virtual boliwere designed in Blender and 3D printed from PLA plastic material. Theconformity of each printed boli was assessed by measuring the air gap volume and approximate thickness between the phantom & bolus acquired together on a CT. For the participant study, photographs, and a LiDAR scan of four volunteers were captured using an iPhone 13 Pro™ to assess their feasibility for generating human models. Each virtual 3D model was visually assessed to identify any issues in their reconstruction. The LiDAR models wereregistered to the photogrammetry models where a distance to agreement analysis was performed to assess their level of similarity.Additionally, a 3D virtual bolus was designed and printed using ABS material from all models to assess their conformity onto the participants skin surface using a verbal feedback method. The photogrammetry derived bolus showed comparable conformity to the CT derived bolus while the LiDAR derived bolus showed poorer conformity as shown by their respective air gap volume and thickness measurements. The reconstruction quality of both the photogrammetry and LiDAR models of the volunteers was inadequate in regions of facial hair and occlusion, which may lead to clinically unacceptable patient-specific device that are created from these areas. All participants found the photogrammetry 3D printed bolus to conform to their nose region with minimal room to move while three of the four participants found the LiDAR was acceptable and could be positioned comfortably over their entire nose. Smartphone-based photogrammetry and LiDAR software show great potential for future use in generating 3D reference models for radiation oncology purposes. Further investigations into whether they can be used to fabricate clinically acceptable patient-specific devices on a larger and more diverse cohort of participants and anatomical locations is required for a thorough validation of their clinical usefulness.

A feature selection method for multimodal multispectral LiDAR sensing

Optical remote sensing techniques can indicate the properties of objects by observing different modalities (physical quantities) of the backscattered light at different optical wavelengths. Established examples are reflectance, fluorescence, Raman, or depolarization spectroscopy. LiDAR sensing, on the other hand, allows acquiring the geometry of objects by measuring the propagation delay of optical probing signals. Multimodal multispectral (MM) LiDAR combines these capabilities and extends conventional monochromatic LiDAR in both spectral and modal dimensions within a single instrument, thus enriching point cloud data with non-geometric information. The potentially high dimension of MM LiDAR data, however, poses significant challenges for instrumental design, data acquisition, and data processing. MM LiDAR data are structured as several or all modalities are available in each of the spectral channels. The above challenges can thus be mitigated by feature selection (FS), if the structure of the features is taken into account, i.e., if entire spectral channels or entire modalities are selected or omitted. Herein, we focus on the feature selection method for MM LiDAR and propose a multiclass group feature selection algorithm (MGSVM FS) consisting of a structural sparsity-based embedded feature selection method with an all-in-one support vector machine (SVM). It tackles jointly the challenges arising from the high dimension of the MM data and the need for a multiclass classification task while exploiting the structure of the MM data. In addition, we introduce a complete workflow for evaluating the feature selection and for decision-making. We apply the framework to selecting an optimum spectral and modal configuration for remote material classification using an experimental MM LiDAR system that provides reflectance, distance, and degree of linear polarization in 28 spectral channels of 10 nm width. For the experimental investigation, we use MM LiDAR data obtained in a controlled lab environment from thirty specimens of four material classes relevant for construction. Using all three modalities, we find a configuration with only 3 spectral channels that achieves a classification mean-F1 score of 100% within this small dataset. Similar classification performance can also be achieved with only two modalities when using more spectral channels. MGSVM FS improves the classification mean-F1 score by up to 25% as compared to random selection and outperforms two other commonly used filter and embedded feature selection methods, in this application example. The proposed group feature selection algorithm and decision-making are useful for MM LiDAR, providing a link between instrumental design, data acquisition, and data processing. However, they are also transferable to other application fields related to multiclass classification, regression, and knowledge discovery, with features structured in groups. The collected MM feature dataset, the MGSVM FS algorithm, and the evaluation pipeline are accessible online.11https://github.com/yuhan-yhyh/Dataset_Code_MGSVM-FS-MM-LiDAR.git.

3-D Objects Detection and Tracking Using Solid-State LiDAR and RGB Camera

Objects detection and tracking using 3D LiDAR has gained momentum lately while it has not been extensively applied, and the main challenge is that conventional mechanical LiDAR is expensive and it is difficult for single sensor to obtain good tracking performance over a long period of time. In this paper, we propose a multi-sensor fusion 3D objects detection and tracking framework using solid-state LiDAR and RGB camera. We use a low-cost solid-state LiDAR with irregular scan pattern and propose a universal clustering method which determines the searching radius by laser density and range. To improve the overall tracking performance, range and visual information are both utilized to associate 3D objects across different frames. We avoid introducing odometry errors into the system and use visual difference rather than estimated trajectory to distinguish between closely located objects. And we have designed a re-detection process to locate objects that were missed by the clustering algorithm. We evaluated the proposed method on five sequences captured by our ego-vehicle and three additional sequences from open-source datasets. Our results demonstrate that the adaptive searching radius enhances recall and the overall tracking performance is improved by fusing LiDAR and camera.

Detection and mapping of specular surfaces using multibounce LiDAR returns.

We propose methods that use specular, multibounce LiDAR returns to detect and map specular surfaces that might be invisible to conventional LiDAR systems that rely on direct, single-scatter returns. We derive expressions that relate the time- and angle-of-arrival of these multibounce returns to scattering points on the specular surface, and then use these expressions to formulate techniques for retrieving specular surface geometry when the scene is scanned by a single beam or illuminated with a multi-beam flash. We also consider the special case of transparent specular surfaces, for which surface reflections can be mixed together with light that scatters off of objects lying behind the surface.

Effectiveness of 2020 Airborne Lidar for Identifying Archaeological Sites and Features on Guåhan (Guam)

Guåhan (Guam), part of the Mariana Islands in the western Pacific, has an archaeological record spanning almost four millennia. In this seminal academic paper on the use of lidar for archaeological investigation on Guåhan, we determined which known sites can be visually detected to explore the effectiveness of lidar in this context. Several archaeological site types and features from the Latte, Spanish, and Modern Periods were examined using high-resolution 2020 airborne lidar. We generated the most commonly used digital elevation models (DEMs), which are digital terrain models (DTMs) and digital surface models (DSMs), and visualised them as a hillshade. Lidar proved effective for identifying all the Modern Period sites and most of the Latte and Spanish Period sites, although some features remained obscure. Major challenges and limitations of using lidar on Guåhan were vegetation, site size, and visual ambiguities. While this study used conventional lidar derivatives, it exposed the potential to find more archaeological sites by applying more complex lidar processing techniques in the future. The benefits of lidar are of great interest to the local community, especially the Indigenous CHamoru, interested in non-destructive ways to assist their cultural heritage management.

Reality Capture for Historic BIM (HBIM) Development of the Old Polk County Courthouse in Bartow, Florida, USA

Nestled in the historic district of the small Central Florida town of Bartow, the Old Polk County Courthouse (OPCC) served the citizens of Polk County from 1909 to 1987. Eventually replaced by a high-rise courthouse to accommodate population growth, the OPCC underwent major restorations between 1993 and 1996 and now serves as the Polk County History Center, which houses a historical museum and genealogical library. Due to its historical value within local and regional contexts, it was deemed important to archive the courthouse in a manner that can be utilized from a practical standpoint of preservation, operations, and maintenance and an academic standpoint for research of early 20th century buildings in the southeastern U.S. A research project has been done to use Historic Building Information Modeling (HBIM) technology to digitally preserve and reconstruct the OPCC building. The findings of this project can be implemented to help digitally document and recreate other historic structures. This paper presents the work flow and technologies that were utilized to capture data for developing HBIM models of the OPCC, including conventional field surveys, LiDAR scanning, photogrammetry, and 360-degree photography.

Low-pixel-count imaging FMCW lidar.

We demonstrate the imaging capability of a frequency modulated continuous wave (FMCW) lidar based on a fiber bundle. The lidar constructs velocity and range images for hard targets at a rate of 60Hz. The sensing range is up to 30m with 20mW output power. The instrument employs custom electronics with seven parallel heterodyne receivers. An example of image recovery is presented on 6-pixel "pictures" of a spinning disk and a drone hovering in the air. In experiments, we also tested the laser tuning linearity correction with a phase-locked loop. We see the practicality of such a low-pixel-count system as a boost in scanning rate of conventional lidars or for direct target imaging with a further upgrade of pixel count.

Generalized LOAM: LiDAR Odometry Estimation With Trainable Local Geometric Features

This letter presents a LiDAR odometry estimation framework called Generalized LOAM. Our proposed method is generalized in that it can seamlessly fuse various local geometric shapes around points to improve the position estimation accuracy compared to the conventional LiDAR odometry and mapping (LOAM) method. To utilize continuous geometric features for LiDAR odometry estimation, we incorporate tiny neural networks into a generalized iterative closest point (GICP) algorithm. These neural networks improve the data association metric and the matching cost function using local geometric features. Experiments with the KITTI benchmark demonstrate that our proposed method reduces relative trajectory errors compared to the other LiDAR odometry estimation methods.

Low‐cost solid‐state LiDAR/inertial‐based localization with prior map for autonomous systems in urban scenarios

AbstractA low‐cost and accurate positioning solution is significant for the massive deployment of fully autonomous driving vehicles (ADV). Conventional mechanical LiDAR has proven its performance, but its high cost hinders the massive production of autonomous vehicles. This paper proposes a low‐cost LiDAR/inertial‐based localization solution for autonomous systems with prior maps in urban areas. Instead of relying on the costly mechanical LiDAR, this paper proposes to utilize the solid‐state LiDAR (SSL) with the prior map to estimate the position of the vehicle by matching the real‐time point clouds from the SSL and the prior map using the normal distribution transformation (NDT) algorithm. However, the field of view (FOV) of the SSL is signifcantly smaller than the conventional mechanical LiDAR, which can easily lead to failure during the NDT map matching. To fill this gap, this paper proposes to exploit the complementariness of the inertial measurement unit (IMU) and the SSL, where the IMU pre‐integration provides a coarse but high‐frequency initial guess to the map matching. To evaluate the effectiveness of the proposed method in this paper, the authors collect the dataset in two typical urban scenarios through a pedestrian hand‐hold and a vehicle driving condition. The results reveal that the SSL‐only‐based localization is significantly challenged in dynamic scenarios. With the help of the IMU, the robustness of the proposed method is significantly improved, achieving an accuracy of within 0.5 m. To show the sensitivity of the SSL‐based map matching against the initial guess of the state, this paper also presents the convergence results of the map matching under different levels of accuracy in terms of the initial guess.

Investigating Suppression of Cloud Return with a Novel Optical Configuration of a Doppler Lidar

The full-width at half-maximum or probe length of the Lorentzian weighting function of continuous-wave Doppler lidars increases quadratically with the focus distance, which results in a deterioration in the spatial resolution of measurements. What is worse, a Doppler lidar is susceptible to moving objects that are far away from the intended measurement point. Therefore, we suggest a novel configuration to mitigate these problems by deploying two co-planar quarter-wave plates with orthogonal fast axes in the conventional continuous-wave lidar system, without any change to the other optical or electronic components. If the vertically polarized laser beam that we emit goes out and its backscattered beam returns back through the same quarter-wave plate, the returned beam will become horizontally polarized. The horizontally polarized backscattered beam cannot beat with the vertically polarized local oscillator to generate a Doppler signal. However, the polarization of the returned beam will remain unchanged if the emitted beam travels out through one plate and returns through the other. In this way, the influence of a moving backscattering particle far away from the focus point can be reduced. Both theoretical and experimental results show that, in a proper configuration, the probe length of the continuous-wave lidar can be reduced by 10%, compared with that of the conventional lidar. In addition, the fat tails of the Lorentzian weighting function can be suppressed by up to 80% to reduce the return from a cloud, albeit with a large reduction (perhaps 90%) in the signal power. This investigation provides a potential method to increase the spatial resolution of Doppler wind lidars and suppress the low-hanging cloud return.

Fast Dual-LiDAR Reconstruction for Dynamic Wind Field Retrieval

With the advantages of high accuracy, high spatial resolution, and long measurement range, LiDAR is considered as the most suitable measurement technique to deliver quantitative imaging of wind fields. However, for complex wind fields, such as monitoring wind turbine wakes where both the temporal resolution and reconstruction speed are of great significance, the conventional LiDAR system lacks the temporal resolution to capture the fast changes of wind turbine wake fields. In this paper, a novel dynamic wind retrieval method is developed to improve temporal resolution using the unsynchronised dual-LiDAR scanning scheme. By exploiting the temporal redundancy information of the LiDAR Line-of-Sight (LoS) data in successive frames, a reduced number of LiDAR scanning points is required for the 2D horizontal wind field retrieval with the help of unsynchronised dual-LiDAR wind scanning scheme, low-rank data up-sampling and a divergence-free regularised wind retrieval algorithm. Numerical simulation is performed to validate the proposed method. Results show that the temporal resolution of LiDAR wind retrieval can be improved by a factor of 2 to 8 and provide acceptable results with good spatial resolution.

Towards a solid‐state light detection and ranging system using holographic illumination and time‐of‐flight image sensing

AbstractLight detection and ranging (LIDAR) systems are finding their way into an increasing number of applications including autonomous vehicles. Image sensors based on single‐photon avalanche diode (SPAD) arrays have demonstrated great promise for LIDAR, typically using flood illumination or raster‐scanning of a small spot. Holographic illumination, based on phase‐only liquid crystal on silicon (LCoS) spatial light modulators, has been successfully applied to display systems such as automotive head up displays (HUDs). It holds great promise as the method of illumination for a LIDAR system. We present a novel solid‐state LIDAR architecture using holographic projection and single‐photon detection. We report a simple experiment that demonstrates proof of concept. The results of the experiment show that the novel architecture can improve the depth accuracy of 3D imaging compared to conventional LIDAR systems. We also indicate several key performance advantages of the novel architecture that will increase as the system scales to higher performance.

A Low-Cost High-Resolution LiDAR System With Nonrepetitive Scanning

Obstacle visualization and detection are essential parts of autonomous driving. The capability of accurately and rapidly collecting 3D information of the surrounding environment plays a key role in driving decisions of autonomous vehicles. As an active detection device, LiDAR enables high-speed and real-time depth information acquisition by emitting laser pulses and then receiving echoes. However, the frame rate and density of the point cloud generated by conventional LiDARs are limited by the fixing trajectory of the beam-steering unit, which cannot satisfy the requirements of various scenes. This paper presented a novel non-repetitive scanning algorithm and a LiDAR prototype using a galvanometer scanner as the beam-steering unit. The algorithm achieves dynamic scanning between frame rate and lateral resolution by the superposition of multiple raw frames with sparser non-repetitive trajectory. The LiDAR was made of off-the-shelf components to limit the cost less than 450 US dollars and extend its application scenarios. Experimental results demonstrated that the proposed LiDAR achieved dynamic frame rate and lateral resolution. Each raw frame of point cloud has a lateral resolution of 190×16 and four raw frames with different trajectories synthesize a superposition frame with quadruple resolution of 190×64. Complex profiles of targets become clear with the superposition of multiple raw frames. Thus, the novel non-repetitive scanning improves the LiDAR performance of detecting obstacles with small sizes and maintains the detection of large obstacles at higher speed.

Correspondenceless scan-to-map-scan matching of homoriented 2D scans for mobile robot localisation

The objective of this study is improving the location estimate of a mobile robot capable of motion on a plane and mounted with a conventional 2D LIDAR sensor, given an initial guess for its location on a 2D map of its surroundings. Documented herein is the theoretical reasoning behind solving a matching problem between two homoriented 2D scans, one derived from the robot’s physical sensor and one derived by simulating its operation within the map, in a manner that does not require the establishing of correspondences between their constituting rays. Two results are proved and subsequently shown through experiments. The first is that the true position of the sensor can be recovered with arbitrary precision when the physical sensor reports faultless measurements and there is no discrepancy between the environment the robot operates in and its perception of it by the robot. The second is that when either is affected by disturbance, the location estimate is bound in a neighbourhood of the true location whose radius is proportional to the affecting disturbance.

DepthLiDAR: Active Segmentation of Environment Depth Map Into Mobile Sensors

This paper presents a novel approach for creating virtual LiDAR scanners through the active segmentation of point clouds. The method employs top-view point cloud segmentation in virtual LiDAR sensors that can be applied to the intelligent behavior of autonomous agents. Segmentation is correlated with the visual tracking of the agent for localization in the environment and point cloud. Virtual LiDAR sensors with different characteristics and positions can then be generated. This method is referred to as the DepthLiDAR approach, and is rigorously evaluated to quantify its performance and determine its advantages and limitations. An extensive set of experiments is conducted using real and virtual LiDAR sensors to compare both approaches. The objective is to propose a novel method to incorporate spatial perception in warehouses, aiming to achieve Industry 4.0. Thus, it is tested in a low-scale warehouse to incorporate realistic features. The analysis of the experiments shows a measurement improvement of 52.24% compared to the conventional LiDAR.

High Spatial and Temporal Resolution Bistatic Wind Lidar

The high-resolution bistatic lidar developed at the Physikalisch-Technische Bundesanstalt (PTB) aims to overcome the limitations of conventional monostatic lidar technology, which is widely used for wind velocity measurements in wind energy and meteorology applications. Due to the large measurement volume of a combined optical transmitter and receiver tilting in multiple directions, monostatic lidar generally has poor spatial and temporal resolution. It also exhibits large measurement uncertainty when operated in inhomogeneous flow; for instance, over complex terrain. In contrast, PTB’s bistatic lidar uses three dedicated receivers arranged around a central transmitter, resulting in an exceptionally small measurement volume. The coherent detection and modulation schemes used allow the detection of backscattered, Doppler shifted light down to the scale of single aerosols, realising the simultaneous measurement of all three wind velocity components. This paper outlines the design details and theory of operation of PTB’s bistatic lidar and provides an overview of selected comparative measurements. The results of these measurements show that the measurement uncertainty of PTB’s bistatic lidar is well within the measurement uncertainty of traditional cup anemometers while being fully independent of its site and traceable to the SI units. This allows its use as a transfer standard for the calibration of other remote sensing devices. Overall, PTB’s bistatic lidar shows great potential to improve the capability and accuracy of wind velocity measurements, such as for the investigation of highly dynamic flow processes upstream and in the wake of wind turbines.

Real-Time Semantic Segmentation of 3D Point Cloud for Autonomous Driving

Autonomous vehicles perceive objects through various sensors. Cameras, radar, and LiDAR are generally used as vehicle sensors, each of which has its own characteristics. As examples, cameras are used for a high-level understanding of a scene, radar is applied to weather-resistant distance perception, and LiDAR is used for accurate distance recognition. The ability of a camera to understand a scene has overwhelmingly increased with the recent development of deep learning. In addition, technologies that emulate other sensors using a single sensor are being developed. Therefore, in this study, a LiDAR data-based scene understanding method was developed through deep learning. The approaches to accessing LiDAR data through deep learning are mainly divided into point, projection, and voxel methods. The purpose of this study is to apply a projection method to secure a real-time performance. The convolutional neural network method used by a conventional camera can be easily applied to the projection method. In addition, an adaptive break point detector method used for conventional 2D LiDAR information is utilized to solve the misclassification caused by the conversion from 2D into 3D. The results of this study are evaluated through a comparison with other technologies.

Optical properties of aerosol and cloud particles measured by a single-line-extracted pure rotational Raman lidar.

Conventional lidar methods for deriving particle optical properties suffer from the fact that two unknowns (backscatter and extinction coefficients) need to be determined from only one lidar equation. Thus, additional assumptions (constant lidar ratio or Ångström relationship) have to be introduced to settle this problem. In contrast, a single-line-extracted pure-rotational-Raman (PRR) lidar method allows the strict retrieval of backscatter and extinction coefficients without additional assumptions. Based on the observations of our single-line-extracted PRR lidar from February 2016 to December 2017, the optical properties (backscatter coefficient, extinction coefficient and lidar ratio) of continental polluted aerosols, dust aerosols, and cirrus cloud particles over Wuhan (30.5°N, 114.4°E) are well characterized. The mean values of the measured lidar ratios are respectively 60 ± 7 sr for continental polluted aerosols, 47 ± 4 sr for dust aerosols and 22 ± 4 sr for cirrus cloud particles. The backscatter and extinction coefficients measured by the single-line-extracted PRR lidar deviate as a whole by 7-13% and 13-16%, respectively, from those retrieved by the traditional Fernald method. The optical properties measured by the single-line-extracted PRR lidar can serve as observational standards for particle optical properties (backscatter/extinction coefficient and lidar ratio) at 532 nm wavelength.

A Method for Fast Acquisition of Photon Counts for SPAD LiDAR

The performance of LiDAR with single-photon avalanche diode (SPAD) detectors can degrade seriously when a shortage of photons occurs in adverse environments. It is desirable for SPAD LiDAR to accumulate enough photons so that reliable target detection is maintained under such conditions. It is proposed in this letter to adopt random laser-emitting schemes, such as stochastic pulse position modulation (SPPM), to have more laser pulses illuminated on targets to increase reflected photons. Accordingly, at the receiver side, a novel method, namely multi-start-multi-stop (MSMS), is proposed to work with SPPM to generate histograms for further processing of target detection and ranging. Theoretical analysis and experiments show that the SPPM+MSMS LiDAR can produce, with far less integration time, signal levels comparable to that of conventional LiDAR.

Low-Cost Retina-Like Robotic Lidars Based on Incommensurable Scanning

High performance light detection and ranging (lidars) are essential in autonomous robots such as self-driving cars, automated ground vehicles and intelligent machines. Traditional mechanical scanning lidars offer superior performance in autonomous vehicles, but the potential mass application is limited by the inherent manufacturing difficulty. In this article, we propose a robotic lidar sensor based on incommensurable scanning that allows straightforward mass production and adoption in autonomous robots. Some unique features are additionally permitted by this incommensurable scanning. Similar to the fovea in human retina, this lidar features a peaked central angular density, enabling in applications that prefers eye-like attention. The incommensurable scanning method of this lidar could also provide a much higher resolution than conventional lidars which is beneficial in robotic applications such as sensor calibration. Examples making use of these advantageous features are demonstrated.