Laser Pointer Research Articles

Laser 3D tightly coupled mapping method based on visual information

PurposeSimultaneous localization and map building (SLAM), as a state estimation problem, is a prerequisite for solving the problem of autonomous vehicle motion in unknown environments. Existing algorithms are based on laser or visual odometry; however, the lidar sensing range is small, the amount of data features is small, the camera is vulnerable to external conditions and the localization and map building cannot be performed stably and accurately using a single sensor. This paper aims to propose a laser three dimensions tightly coupled map building method that incorporates visual information, and uses laser point cloud information and image information to complement each other to improve the overall performance of the algorithm.Design/methodology/approachThe visual feature points are first matched at the front end of the method, and the mismatched point pairs are removed using the bidirectional random sample consensus (RANSAC) algorithm. The laser point cloud is then used to obtain its depth information, while the two types of feature points are fed into the pose estimation module for a tightly coupled local bundle adjustment solution using a heuristic simulated annealing algorithm. Finally, the visual bag-of-words model is fused in the laser point cloud information to establish a threshold to construct a loopback framework to further reduce the cumulative drift error of the system over time.FindingsExperiments on publicly available data sets show that the proposed method in this paper can match its real trajectory well. For various scenes, the map can be constructed by using the complementary laser and vision sensors, with high accuracy and robustness. At the same time, the method is verified in a real environment using an autonomous walking acquisition platform, and the system loaded with the method can run well for a long time and take into account the environmental adaptability of multiple scenes.Originality/valueA multi-sensor data tight coupling method is proposed to fuse laser and vision information for optimal solution of the positional attitude. A bidirectional RANSAC algorithm is used for the removal of visual mismatched point pairs. Further, oriented fast and rotated brief feature points are used to build a bag-of-words model and construct a real-time loopback framework to reduce error accumulation. According to the experimental validation results, the accuracy and robustness of the single-sensor SLAM algorithm can be improved.

Intensity Stability Comparison Between Different Colors of Laser Pointer

Different source of light can be used as a main light source in an open path optical method. Sunlight, tungsten lamp, LED, laser, mercury vapor lamp and halogen are the example of the light source.It is important to test the stability of the light source used to avoid inaccuracy in the measurement. The purpose of this project is to determine which color of laser has higher stability. Once the light is switched on, the intensity of the light source changes slowly until it comes to a stable state. Each color has different counts of intensity and stability. Different set of laser pointer is used in this project which included blue, violet, red and green color. An open path optical method is used in the experiment. A different color of laser pointer will be used as a light source whilespectrometer will act as a detector. The stability for each color of laser pointer is reported in this paper where the standarddeviationforeachcoloriscalculated todetermined whichcolorhaveahigherstability.The standard deviation for green color has the smallest value among these four colors where the standard deviation is 15.97.Colorwithsmallerstandarddeviationhasthehigheststability.

We need to talk

We need to talk

Sensitive, specific, smartphone-based quantitative sensing of glyphosate by integrating analyte-triggered anti-aggregation/anti-autocatalysis of metal nanoparticles with Tyndall-effect colorimetric signaling

This work initially describes a new class of pesticide nanosensor for highly sensitive, equipment-free detection of glyphosate as a model analyte by using Tyndall effect (TE) for colorimetric signaling. In the absence of analyte, o-phenylenediamine (OPD) can be oxidized by bivalent copper (Cu2+) ions into 2, 3-diaminophenazine (OPDox), from which copper nanoparticles (CuNPs) are also formed to further act as nanocatalysts for catalyzing the above redox reactions. The resultant large number of positively-charged OPDox products can make citrate-capped gold NPs (AuNPs) aggregate via N–Au chemistry and electrostatic forces. With a 635-nm laser pointer pen, both the CuNPs and aggregated AuNPs can efficiently scatter its red light beam, leading to a significantly-enhanced TE response (compared with the dispersed AuNPs). For the glyphosate sample, however, only a weak TE signal can be recorded, because the analyte can chelate with the Cu2+ ions to inhibit the OPDox production, the generation/antocatalysis of CuNPs, and the OPDox-induced AuNPs’ aggregation. The glyphosate level is inversely proportional to the TE signal which can be measured by a smartphone for quantitative analysis. The results show that the developed nanosensor enables instrument-free detection of glyphosate with a limit down to 117 nM. To the best of our knowledge, this may be the first report of designing enhanced TE-based colorimetric nanosensors with two types of colloidal nanoparticles for potential on-site detection of pesticide residue in various fields like food safety and environmental monitoring.

Dynamic Progress Monitoring of Masonry Construction through Mobile SLAM Mapping and As-Built Modeling

Traditional progress monitoring can be inaccurate and time-consuming, potentially causing time delay and cost overrun in construction projects. With development in technology, tools such as cameras, laser scanners, and building information modelling (BIM) have been used to overcome existing problems in the traditional approach. However, noise mitigation, extracting objects of interest from laser point clouds, and detailed progress measurement are problems that still exist. In this study a novel method of construction progress monitoring to measure the progress percentage is presented. The study integrates the simultaneous localization and mapping (SLAM) technique with as-built BIM to gather quick and accurate construction site progress information. The Hausdorff distance is utilized to extract objects of interest and filter out noise from site-scan data. As-built and as-planned BIM models are compared using Python and Dynamo, to obtain progress percentage. A case study was conducted on a residential building located in Sydney, Australia, to validate the application of the developed method. The outcome demonstrates that utilizing the SLAM technique and Hausdorff distance are effective in mitigating noise and extracting objects of interest from site-scan data, respectively. In addition, with an accuracy of 94.67 percent in estimation, the progress percentage was obtained based on material quantities. The obtained progress percentage could also be used in updating construction schedules and assisting decision-making.

Head-Mounted Miniature Motorized Camera and Laser Pointer Driven by Eye Movements

Recording a video scene as seen by an observer, materializing where is focused his visual attention and allowing an external person to point at a given object in this scene, could be beneficial for various applications such as medical education or remote training. Such a versatile device, although tested at the experimental laboratory demonstrator stage, has never been integrated in a compact and portable way in a real environment. In this context, we built a low-cost, light-weight, head-mounted device integrating a miniature camera and a laser pointer that can be remotely controlled or servo-controlled by an eye tracker. Two motorizations were implemented and tested (pan/tilt and Rilsey-prisms-based). The video was both recorded locally and transmitted wirelessly. Risley prisms allowed finer remote control of camera or laser pointer orientation (0.1° vs. 0.35°), but data processing and Wi-Fi transmission incur significant latency (~0.5 s) limiting the servo-controlling by eye movements. The laser beam was spatially shaped by a Diffractive Optical Element to facilitate object illumination or recognition. With this first proof-of-concept prototype, the data stream needs to be optimized to make full use of the eye tracker, but this versatile device can find various applications in education, healthcare or research.

Comparing mobile and terrestrial laser scanning for measuring and modelling tree stem taper

Abstract Measuring and modelling the shape of tree stems is a fundamental component of forest inventory systems for both commercial and biological purposes. The change in diameter of the stem along its length (a.k.a. 'taper') is one of the most important and widely used means of predicting tree stem volume. Until recently, the options for obtaining accurate estimates of stem taper and developing stem taper models have been limited to measurements of felled trees or the use of optical dendrometers on standing live trees. Here, we tested both a tripod-mounted terrestrial laser scanner (TLS; a Focus 3D 120 of FARO Technologies, Inc., Lake Mary, FL, USA), and a mobile laser scanner (MLS; the ZEB1 of the GeoSLAM Ltd, Nottingham, UK) to measure tree diameters at various heights along the stem of 20 destructively harvested broadleaf and needleleaf species using the outer hull modelling method, for the purpose of developing individual-tree and species-specific taper models. Laser scanner specifications were a major factor determining stem taper measurement accuracy. The longer-range, low beam divergence TLS could estimate stem diameter to an average of 15.7 m above ground (about 79 per cent of the canopy height), while the shorter-range high beam divergence MLS could estimate an average of 11.5 m above ground (about 45 per cent of the canopy height). Stem taper error increased with respect to height above ground, with the TLS providing more consistent and reliable diameter measurements (root mean square error (RMSE) = 1.93 cm; 9.57 per cent) compared with the MLS (RMSE = 2.59 cm; 12.84 per cent), but both methods were nearly unbiased. We attribute ~60 per cent of the uncertainty in stem measurements to laser beam diameter and point density, showing positive and negative correlations, respectively. MLS was unable to converge on the two tested taper models but was found to be an efficient means of easily sampling diameters at breast height (DBH) and reconstructing stem maps in simple forest stands with trees greater than ~10 cm DBH. TLS provided precision stem diameter measurements that allowed for the creation of similar taper models for three out of the four study species. Future work should focus on evaluating MLS systems with improved specifications (e.g. beam divergence and range), since these instruments will likely lead to dramatic improvements in reliable estimates of forest inventory parameters, in line with the current TLS technology.

Effects of trunk stabilization exercises using laser pointer visual feedback in patients with chronic stroke: A randomized controlled study.

Many previous studies have cited the importance of trunk stabilization exercises in patients with stroke. However, the evidence for optimal trunk stabilization exercises for patients with stroke is still lacking. To investigate the effects of laser pointer visual feedback in trunk stabilization exercises that are important for improving trunk dysfunction in patients with stroke. In total, 30 patients with chronic stroke were randomly assigned to experimental and control groups. The experimental group underwent a traditional stroke rehabilitation program and trunk stabilization exercises using laser pointer visual feedback. The control group underwent a traditional stroke rehabilitation program and trunk stabilization exercises without visual feedback. Pre- and postintervention results after 6weeks were evaluated using the Berg Balance Scale, static and dynamic plantar pressure, 10-m walk test, and the Korean version of the Fall Efficacy Scale. The results were analyzed using a general linear repeated measurement model. Both groups showed significant improvements in BBS scores, static plantar pressure, dynamic plantar pressure, 10 MWT, and K-FES scores after 6weeks of intervention (P< 0.05). Compared to the control group, significant improvements were observed in the experimental group in the Berg Balance Scale scores, dynamic paretic posterior plantar pressure, 10-m walk test, and Korean version of the Fall Efficacy Scale scores (P< 0.025). Our results demonstrated the effectiveness of visual feedback during trunk stabilization exercises for resolving trunk dysfunction in patients with stroke. Trunk stabilization exercises using laser pointer visual feedback have been found to be more effective in balance, walking, and fall efficacy in patients with stroke.

Road classification using 3D LiDAR sensor on vehicle

Road classification is essential in outdoor vehicle perception systems to evaluate driving scenarios. A light detection and ranging (LiDAR) sensor can provide accurate measurements of an environment composed of range, intensity, and angular information. This study proposes a weakly supervised learning algorithm for classifying road surface types based on laser measurements. The proposed algorithm first separates the laser points on the road surface based on the angular structure of the initial range measures obtained using 3D LiDAR in a noniterative manner. Then, a physical-data-driven model that represents the retroreflective nature of the different surface types is abstracted from the intensity measures and their factors. The linear regression parameters of the model are employed as feature descriptions of the road surface. The K-means method is used to classify the different road types. The learning process requires weak supervision at the training centroid stage and is robust to new road types. The classification performance was evaluated in different outdoor scenarios, and the classification accuracies exceeded 98%. The proposed algorithm outperforms similar existing methods, performs at the same frequency as that of LiDAR, and can be implemented in real time.

Laser Radar Data Registration Algorithm Based on DBSCAN Clustering

At present, the core of lidar data registration algorithms depends on search correspondence, which has become the core factor limiting the performance of this kind of algorithm. For point-based algorithms, the data coincidence rate is too low, and for line-based algorithms, the method of searching the correspondence is too complex and unstable. In this paper, a laser radar data registration algorithm based on DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering is proposed, which avoids the search and establishment of the corresponding relationship. Firstly, a ring band filter is designed to process the outliers with noise in a point cloud. Then, the adaptive threshold is used to extract the line segment features in the laser radar point cloud. For the point cloud to be registered, a DBSCAN density clustering algorithm is used to obtain the key clusters of the rotation angle and translation matrix. In order to evaluate the similarity of the two frames of the point cloud in the key clusters after data registration, a kernel density estimation method is proposed to describe the registered point cloud, and K-L divergence is used to find the optimal value in the key clusters. The experimental results show that the proposed algorithm avoids the direct search of the correspondence between points or lines in complex scenes with many outliers in laser point clouds, which can effectively improve the robustness of the algorithm and suppress the influence of outliers on the algorithm. The relative error between the registration result and the actual value is within 10%, and the accuracy is better than the ICP algorithm.

Semi-automated Generation of Geometric Digital Twin for Bridge Based on Terrestrial Laser Scanning Data

Digital twins (DTs) have a great potential for bridge operation and maintenance. Geometric digital twins (gDTs) are the key component of DTs. At present, a growing number of researchers are using high-precision 3D laser point clouds to generate gDTs. However, for large bridges, such as arch, cable-stayed, and suspension bridges, comprehensive point-cloud collection stations are difficult to set up due to their large span, narrow site, and limited field of vision. Consequently, the complete point clouds of these bridges cannot be easily obtained. Thus, knowing how to process absence point clouds and generate gDTs is an urgent problem. This study proposes a semiautomatic method of extracting geometric information of a bridge’s components in the absence of point clouds. First, an algorithm based on the combination of the iterative polynomial fitting curve and sliding window is developed to extract the arch ring accurately. Second, an improved random sample consensus (RANSAC) algorithm based on distribution density is adopted to extract the cross sections of the arch bridge components, except the arch ring. For cross sections that lack point clouds, a translation strategy is used to supplement the unknown line segment. Finally, for the T-beam, a model alignment method is proposed to best match the characteristic intersections extracted by the improved RANSAC algorithm and the points corresponding to the design model. The quality of the generated models is gauged using a point cloud deviation chromatogram. In addition, the stressed component piers are compared with its design parameters to verify the accuracy of the proposed method. Results show that our method can efficiently and accurately extract geometric information and generate gDT for the bridge.

A Slope Structural Plane Extraction Method Based on Geo-AINet Ensemble Learning with UAV Images

In the construction of large-scale water conservancy and hydropower transportation projects, the rock mass structural information is often used to evaluate and analyze various engineering geological problems such as high and steep slope stability, dam abutment stability, and natural rock landslide geological disasters. The complex shape and extremely irregular distribution of the structural planes make it challenging to identify and extract automatically. This study proposes a method for extracting structural planes from UAV images based on Geo-AINet ensemble learning. The UAV images of the slope are first used to generate a dense point cloud through a pipeline of SfM and PMVS; then, the multiple geological semantics, including color and texture from the image and local geological occurrence and surface roughness from the dense point cloud, are integrated with Geo-AINet for ensemble learning to obtain a set of semantic blocks; finally, the accurate extraction of structural planes is achieved through a multi-semantic hierarchical clustering strategy. Experimental results show that the structural planes extracted by the proposed method perform better integrity and edge adherence than that extracted by the AINet algorithm. In comparison with the results from the laser point cloud, the geological occurrence differences are less than three degrees, which proves the reliability of the results. This study widens the scope for surveying and mapping using remote sensing in engineering geological applications.

Reliability and Validity of A Novel Device for Evaluating the Cervical Proprioception.

In clinical practice, cervical proprioception is usually evaluated by calculating the cervical joint position error (JPE) with laser pointer devices (LPD) or cervical range-of-motion (CROM) instruments. As technology continues to improve, more and more advanced tools are used to evaluate cervical proprioception. The purpose of this study was to analyze the reliability and validity of the WitMotion sensor (WS) device in evaluating cervical proprioception, and to explore a cheaper, more convenient, and more practical testing tool. Twenty-eight healthy participants (16 women, 12 men; age 25-66years) were recruited and evaluated for cervical joint position error with a WS and LPD by two independent observers. All participants repositioned their head to the target position and the deviation of repositioning was calculated using these two instruments. The intra- and inter-rater reliability of the instrument were determined by calculating the intraclass correlation coefficients (ICC), and the validity was analyzed by calculating the ICC and the Spearman's correlation. The intra-rater reliability of the WS (ICCs = 0.682-0.774) was higher than that of the LPD (ICCs = 0.512-0.719) for measuring JPE of cervical flexion, right lateral flexion, and left rotation. However, the LPD (ICCs = 0.767-0.796) outperformed the WS (ICCs = 0.507-0.661) in cervical extension, left lateral flexion, and right rotation. For the inter-rater reliability, the ICC values obtained by the WS and the LPD were above 0.70 for all cervical movements except cervical extension and left lateral flexion (ICCs = 0.580-0.679). For the validity, the ICC values were moderate to good (ICCs > 0.614) for measuring JPE in all movements with the WS and the LPD. Based on the high ICC values of reliability and validity, the novel device can be an alternative tool to evaluate cervical proprioception in clinical practice. This study was registered in the Chinese Clinical Trial Registry (ChiCTR2100047228).

Horizontal-plane localization of a target in the presence of a distractor: Effect of onset asynchrony

Listeners localized a target in isolation and in the presence of a distractor. The target consisted of 10 10-ms Gaussian pulses (energy between 3–5kHz); the distractor was either a 100- or 500-ms narrowband (1–2 kHz) noise burst. The listener, with head unrestrained, was seated in the center of 36 loudspeakers spaced every 10 degrees on the horizontal plane and obscured by screens. On each trial, the target was played through a randomly selected loudspeaker. Listeners used a laser pointer to indicate target location. Pointer position was detected by infrared cameras and the response angle calculated. In separate conditions, distractor position was either fixed or randomized across trials, with target-to-distractor onset synchrony an additional parameter. The left-right component of responses was essentially unaffected by the presence of the distractor in any condition. With notable inter-subject variability, the front-back response component also showed little effect of the distractor. This absence of consistent effect of the distractor contrasts with previously reported work. The target and distractor in the present research, however, were generally more discriminable than in past studies, which likely mitigated at least some of the deleterious effect of the distractor on target localization ability.

Green Laser Pointer–Induced Subinternal Limiting Membrane Hemorrhage

Green Laser Pointer–Induced Subinternal Limiting Membrane Hemorrhage

Geometric positioning integrating optical satellite stereo imagery and a global database of ICESat-2 laser control points: A framework and key technologies

ABSTRACT Block Adjustment (BA) is one of the essential techniques for producing high-precision geospatial 3D data products with optical stereo satellite imagery. For block adjustment with few ground-control points or without ground control, the vertical error of the model is the decisive factor that constrains the accuracy of 3D data products. The elevation data obtained by spaceborne laser altimeter have the advantages of short update periods, high positioning precision, and low acquisition cost, providing sufficient data support for improving the elevation accuracy of stereo models through the combined BA. This paper proposes a geometric positioning model based on the integration of Optical Satellite Stereo Imagery (OSSI) and spaceborne laser altimeter data. Firstly, we elaborate the principle and necessity of this work through a literature review of existing methods. Then, the framework of our geo-positioning models. Secondly, four key technologies of the proposed model are expounded in order, including the acquisition and management of global Laser Control Points, the association of LCPs and OSSI, the block adjustment model combining LCPs with OSSI, and the accuracy estimation and quality control of the combined BA. Next, the combined BA experiment using Ziyuan-3 (ZY-3) OSSI and ICESat-2 laser data was carried out at the testing site in Shandong Province, China. Experimental results prove that our method can automatically select LCPs with high accuracy. The elevation deviation of the combined BA eventually achieved the Mean Error (ME) of 0.06 m and the Root Mean Square Error (RMSE) of 1.18 m, much lower than the ME of 13.20 m and the RMSE of 3.88 m before the block adjustment. A further research direction will be how to perform more adequate accuracy analysis and quality control using massive laser points as checkpoints.

Multi-Sensor Data Fusion for 3D Reconstruction of Complex Structures: A Case Study on a Real High Formwork Project

As the most comprehensive document types for the recording and display of real-world information regarding construction projects, 3D realistic models are capable of recording and displaying simultaneously textures and geometric shapes in the same 3D scene. However, at present, the documentation for much of construction infrastructure faces significant challenges. Based on TLS, GNSS/IMU, mature photogrammetry, a UAV platform, computer vision technologies, and AI algorithms, this study proposes a workflow for 3D modeling of complex structures with multiple-source data. A deep learning LoFTR network was used first for image matching, which can improve matching accuracy. Then, a NeuralRecon network was employed to generate a 3D point cloud with global consistency. GNSS information was used to reduce search space in image matching and produce an accurate transformation matrix between the image scene and the global reference system. In addition, to enhance the effectiveness and efficiency of the co-registration of the two-source point clouds, an RPM-net was used. The proposed workflow processed the 3D laser point cloud and UAV low-altitude multi-view image data to generate a complete, accurate, high-resolution, and detailed 3D model. Experimental validation on a real high formwork project was carried out, and the result indicates that the generated 3D model has satisfactory accuracy with a registration error value of 5 cm. Model comparison between the TLS, image-based, data fusion 1 (using the common method), and data fusion 2 (using the proposed method) models were conducted in terms of completeness, geometrical accuracy, texture appearance, and appeal to professionals. The results denote that the generated 3D model has similar accuracy to the TLS model yet also provides a complete model with a photorealistic appearance that most professionals chose as their favorite.

Accurate Extraction of Cableways Based on the LS-PCA Combination Analysis Method

In order to maintain a ski resort efficiently, regular inspections of the cableways are essential. However, there are some difficulties in discovering and observing the cable car cableways in the ski resort. This paper proposes a high-precision segmentation and extraction method based on the 3D laser point cloud data collected by airborne lidar to address these problems. In this method, first, an elevation filtering algorithm is used to remove ground points and low-height vegetation, followed by preliminary segmentation of the cableway using the spatial distribution characteristics of the point cloud. The ropeway segmentation and extraction are then completed using the least squares-principal component combination analysis method for parameter fitting. Additionally, we selected three samples of data from the National Alpine Ski Center to be used as test objects. The real value is determined by the number of point clouds manually deducted by CloudCompare. The extraction accuracy is defined as the ratio of the number of point clouds extracted by the algorithm to the number of point clouds manually extracted. While the environmental complexities of the samples differ, the algorithm proposed in this paper is capable of segmenting and extracting cableways with great accuracy, achieving a comprehensive and effective extraction accuracy rate of 90.59%, which is sufficient to meet the project’s requirements.

PR-Alignment: Multidimensional Adaptive Registration Algorithm Based on Practical Application Scenarios

In the present day, 3D point clouds are considered to be an important form of representing the 3D world. In computer vision, mobile robotics, and computer graphics, point cloud registration is a basic task, and it is widely used in 3D reconstruction, reverse engineering, among other applications. However, the mainstream method of point cloud registration is subject to the problems of a long registration time as well as a poor modeling effect, and these two factors cannot be balanced. To address this issue, we propose an adaptive registration mechanism based on a multi-dimensional analysis of practical application scenarios. Through the use of laser point clouds and RGB images, we are able to obtain geometric and photometric information, thus improving the data dimension. By adding target scene classification information to the RANSAC algorithm, combined with geometric matching and photometric matching, we are able to complete the adaptive estimation of the transformation matrix. We demonstrate via extensive experiments that our method achieves a state-of-the-art performance in terms of point cloud registration accuracy and time compared with other mainstream algorithms, striking a balance between expected performance and time cost.

Development of paper-immobilized molecularly imprinted polymers by laser pointer activation for methamphetamine extraction with analysis by ion mobility spectrometry

A fast, simple, cheap, and versatile strategy has been proposed for the synthesis of paper-immobilized molecularly imprinted polymers (MIPs) by photoactivated bulk polymerization over a piece of nitrocellulose using a 405 nm laser pointer. Polymerization was carried out using a mixture of methacrylic acid and ethylene glycol dimethacrylate, using methamphetamine as template molecule and bis(2,4,6-trimethylbenzoyl)phenylphosphine oxide as radical initiator. After investigation of different polymerization parameters, the following experimental conditions were found to give best results: size of nitrocellulose strip (13.5 × 4.0 × 0.8 mm), type of porogen (acetonitrile), polymerization mixture volume (75 µL), and irradiation times (10 min). Experimental conditions (such as sample pH, extraction and desorption time, and type and volume of desorption solvent) were also adjusted for the extraction of methamphetamine using the proposed paper-MIP. Methamphetamine determination was carried out by ion mobility spectrometry providing a limit of detection of 14 µg L−1 and quantitative recoveries from 81 to 95% using spiked urine and oral fluid samples. The proposed paper-immobilized MIP device allows a simple and selective sample extraction procedure for the determination of methamphetamine in oral fluids and urine with a high portability, minimal solvent consumption, and reduced costs compared to other conventional approaches.