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Unmanned Aerial Vehicles (UAVs) encounter wind gusts during outdoor operations, impacting their position holding, particularly for quadrotors. This vulnerability is amplified during the autonomous docking to outdoor charging stations. The integration of real-time wind preview information for UAV gust rejection control has become more feasible with advances in remote wind sensor technologies like LiDAR. In this study, a ground-based LiDAR system is proposed to predict wind gusts at the landing site of quadrotors. The acquired wind preview data are subsequently utilized by the Model Predictive Control (MPC) to effectively mitigate disturbances. To validate the proposed methodology, a nonlinear simulation environment has been established using LiDAR data collected from comprehensive field tests. The results demonstrate a notable improvement in the system performance compared to benchmark results. This research underscores the practical utility of real-time wind preview information, facilitated by LiDAR technology, in enhancing the overall operational resilience of UAVs, especially quadrotors, during challenging environmental conditions.

We investigate the use of multiple scattering via Multiple-Field-Of-View (MFOV) lidar signals to characterize bioaerosol particles size and concentration from ground based lidar over distances shorter than a few kilometers. The MFOV lidar signal is calculated for background aerosols at a wavelength 355 nm for a visibility of 30 km. The optical depths studied are small and the calculations are restricted to second order scattering. Also since background aerosols are constituted of relatively small particles which diffuse the light at large angles, the fields of view (FOV) range from 1 to 100 mrad full angle. We show that the MFOV lidar measurements contain exploitable information on particle size and extinction.

Standoff LIDAR detection of BW agents depends on accurate knowledge of the infrared and ultraviolet optical elastic scatter (ES) and ultraviolet fluorescence (UVF) signatures of bio-agents and interferents. MIT Lincoln Laboratory has developed the Standoff Aerosol Active Signature Testbed (SAAST) for measuring polarization-dependent ES cross sections from aerosol samples at all angles including 180° (direct backscatter) [1]. Measurements of interest include the dependence of the ES and UVF signatures on several spore production parameters including growth medium, sporulation protocol, washing protocol, fluidizing additives, and degree of aggregation. Using SAAST, we have made measurements of the polarization-dependent ES signature of Bacillus globigii (atropheaus, Bg) spores grown under different growth methods. We have also investigated one common interferent (Arizona Test Dust). Future samples will include pollen and diesel exhaust. This paper presents the details of the apparatus along with the results of recent measurements.

Identification of aerosol type and chemical composition may help to trace their origin and estimate their impact on land and people. Aerosols chemical composition, size distribution and particles shape, manifest themselves in their spectral scattering cross-section. In order to make a reliable identification, comprehensive spectral analysis of aerosol scattering should be carried out. Usually, spectral LIDAR measurements of aerosols are most efficiently performed using an Nd:YAG laser transmitter in the fundamental frequency and its 2^nd, 3^rd and 4^th harmonics. In this paper we describe automatic detection and identification of several aerosol types and size distributions, using a multi-spectral lidar system operating in the IR, NIR and UV spectral regions. The LIDAR transmitter is based on a single Nd:YAG laser. In addition to the 3^rd and 4^th harmonics in the UV, two optical parametric oscillator units produce the eye-safe 1.5 µm wavelength in the near IR and up to 40 separable spectral lines in the 8-11 µm IR. The combination of a wide spectral coverage required for backscattering analysis combined with fluorescence data, enable the generation of a large spectral data set for aerosols identification. Several natural and anthropogenic aerosol types were disseminated in controlled conditions, to test system capabilities. Reliable identification of transient and continuous phenomena demands fast and efficient control and detection algorithms. System performance, using the specially designed algorithms, is described below.

A method for interpreting elastic-lidar return signals in heavily-polluted atmospheres is presented. It is based on an equation derived directly from the classic lidar equation, which highlights gradients of the atmospheric backscattering properties along the laser optical path. The method is evaluated by comparing its results with those obtained with the differential absorption technique. The results were obtained from locating and ranging measurements in pollutant plumes and contaminated environments around central México.

Lidar is an active remote sensing instrument, but its effective range is often limited by signal-to-noise (SNR) ratio. The reason is that noises or fluctuations always strongly affect the measured results. To resolve this problem, a novel approach of using least-squares support vector machine (LS-SVM) to reconstruct the Lidar signal is proposed in this paper. LS-SVM has been proven as robust to noisy data; the Lidar signal, which is strongly corrupted by noises or fluctuations, can be thought as a function of distance. So detecting Lidar signals from high noisy regime can be regarded as a robust regression procedure which involves estimating the underlying relationship from detected signal data set. To apply the LS-SVM on Lidar signal regression, firstly the noises in Lidar signal is analyzed and then the traditional LS-SVM algorithm is modified to incorporate the a priori knowledge of the Lidar signal in the training of LS-SVM. The experimental results demonstrate the effectiveness and efficiency of our approach.

Automated unmanned vehicles can carry equipment for teams, greatly reducing the load of person. Most of the working environment of unmanned vehicles is in the field. The identification of persons in a cross-country environment is the basic requirement for automated driving vehicles, and the problem of identification has received much attention. Aiming at the problem of person identification from lidar point cloud data, particularly the special problem of identification in a cross-country environment, this paper designs an improved identification algorithm based on Euclidean clustering, theoretical analysis, and the geometric and physical characteristics of people. Experiments are carried out on tracked vehicle platforms in a cross-country environment to verify the performance of the algorithm. The experimental results show that the designed lidar personnel identification algorithm can accurately identify personnel using lidar point cloud data, and the recognition rate is about 5% higher than that before the improvement.

The recognition and detection of 3D point cloud data are important research tools in the field of computer vision, with important applications in many significant fields, such as in unmanned driving, high-precision mapping, and robot-assisted vision. At the same time, with the development of deep learning technology, research on the recognition and detection of 3D point cloud data combined with deep learning technology is receiving more and more attention. One of the main problems with the current self-driving cars is that the detection ability of the optical radar’s echo can be affected by bad weather such as heavy rain, snow, thick smoke, or thick fog. The real signal is attenuated and often very weak or submerged in a large amount of noise, which affects its judgment of the outside environment, meaning that the autonomous vehicle is unable to move. Therefore, it is urgent to solve this problem to improve the accuracy of post-stereoscopic images. This study uses LiDAR to collect point cloud data, and then applies PointNet for deep learning training. Random noise added to the original point cloud data is filtered out with a filter. The accuracy of the original signal state and the signal after filtering the noise is compared. There is an improvement of 60.8% in the method detailed in this study. This method can be widely developed and applied to improve the LiDAR technology in the future.

Advanced driver assistance systems improve driving safety and comfort by applying onboard sensors to collect environmental data, analyze environmental data and decision making. Therefore, advanced driver assistance systems have high requirements for distance perception of the environment. Perceptual sensors commonly used in traditional solutions include stereo vision sensors and the Light Detection and Ranging (LiDAR) sensors. This paper proposes a multi-sensing sensor fusion method for disparity estimation, which combines the perceptual data density characteristics of stereo vision sensors and the measurement accuracy characteristics of LiDAR sensors. The method enhances the sensing accuracy by ensuring high-density sense, which is suitable for distance sensing tasks in complex environments. This paper demonstrates with experimental results on real data that our proposed disparity estimation method performs well and is robust in different scenarios.

This paper describes and compares three different approaches to estimate simultaneous localization and mapping (SLAM) in dynamic outdoor environments. SLAM has been intensively researched in recent years in the field of robotics and intelligent vehicles, many approaches have been proposed including occupancy grid mapping method (Bayesian, Dempster-Shafer and Fuzzy Logic), Localization estimation method (edge or point features based direct scan matching techniques, probabilistic likelihood, EKF, particle filter). In this paper, a number of promising approaches and recent developments in this literature have been reviewed firstly in this paper. However, SLAM estimation in dynamic outdoor environments has been a difficult task since numerous moving objects exist which may cause bias in feature selection problem. In this paper, we proposed a possibilistic SLAM with RANSAC approach and implemented with three different matching algorithms. Real outdoor experimental result shows the effectiveness and efficiency of our approach.

At present, visual simultaneous localization and mapping is a hot topic in the field of unmanned systems, which is popular among academic workers because of its advantages of accurate localization, low cost, large amount of information, and wide range of applications, but it still has some problems, including the camera’s vulnerability to the number of feature points and the noise impact of the inertial measurement unit during uniform linear motion. In response to the above problem this paper carries out the research on multi-sensor fusion localization algorithm, the main work is as follows: Based on ORB-SLAM3, a visual-inertial-laser SLAM algorithm is designed. The relative motion of laser location between image frames is obtained from the data of 2D Lidar and laser height sensor. The relative motion of inertial measurement unit between image frames is obtained from inertial measurement unit preintegration. Based on the method of factor graph optimization, the pose of image frame is optimized by reprojection of map point, relative motion increment of inertial measurement unit, and relative motion increment of laser location. The algorithm improves the localization accuracy by about 24.4% over the ORB-SLAM3 visual mode and about 22.6% over the ORB-SLAM3 visual-inertial mode on the data of the UAV physical platform.

A novel use of the LiDAR sensor of a smartphone spring in introductory physics experiments is discussed in this paper. We have determined the spring constant for various combinations of springs using the LiDAR sensor of a smartphone through the phyphox application. An electrical heater coil is used as a spring, and the period of oscillation of a vertical spring–mass system is measured using a LiDAR sensor. The experimental values of spring constants agree with the theoretical values. A high school student can perform this simple experiment in a smart way at home.

This chapter introduces a novel approach to tree detection by fusing LiDAR (Light Detection and Ranging) and RGB imagery, leveraging Ordered Weighted Averaging (OWA) aggregation operators to improve image fusing. It focuses on enhancing tree detection and classification by combining LiDAR’s structural data with the spectral details from RGB images. The fusion methodology aims to optimize information retrieval, employing image segmentation and advanced classification techniques. The effectiveness of this method is demonstrated on the PNOA dataset, highlighting its potential for supporting forest management.

We investigate the use of multiple scattering via Multiple-Field-Of-View (MFOV) lidar signals to characterize bioaerosol particles size and concentration from ground based lidar over distances shorter than a few kilometers. The MFOV lidar signal is calculated for background aerosols at a wavelength 355 nm for a visibility of 30 km. The optical depths studied are small and the calculations are restricted to second order scattering. Also since background aerosols are constituted of relatively small particles which diffuse the light at large angles, the fields of view (FOV) range from 1 to 100 mrad full angle. We show that the MFOV lidar measurements contain exploitable information on particle size and extinction.

Standoff LIDAR detection of BW agents depends on accurate knowledge of the infrared and ultraviolet optical elastic scatter (ES) and ultraviolet fluorescence (UVF) signatures of bio-agents and interferents. MIT Lincoln Laboratory has developed the Standoff Aerosol Active Signature Testbed (SAAST) for measuring polarization-dependent ES cross sections from aerosol samples at all angles including 180° (direct backscatter) [1]. Measurements of interest include the dependence of the ES and UVF signatures on several spore production parameters including growth medium, sporulation protocol, washing protocol, fluidizing additives, and degree of aggregation. Using SAAST, we have made measurements of the polarization-dependent ES signature of Bacillus globigii (atropheaus, Bg) spores grown under different growth methods. We have also investigated one common interferent (Arizona Test Dust). Future samples will include pollen and diesel exhaust. This paper presents the details of the apparatus along with the results of recent measurements.

Identification of aerosol type and chemical composition may help to trace their origin and estimate their impact on land and people. Aerosols chemical composition, size distribution and particles shape, manifest themselves in their spectral scattering cross-section. In order to make a reliable identification, comprehensive spectral analysis of aerosol scattering should be carried out. Usually, spectral LIDAR measurements of aerosols are most efficiently performed using an Nd:YAG laser transmitter in the fundamental frequency and its 2^nd, 3^rd and 4^th harmonics. In this paper we describe automatic detection and identification of several aerosol types and size distributions, using a multispectral lidar system operating in the IR, NIR and UV spectral regions. The LIDAR transmitter is based on a single Nd:YAG laser. In addition to the 3^rd and 4^th harmonics in the UV, two optical parametric oscillator units produce the eye-safe 1.5 μm wavelength in the near IR and up to 40 separable spectral lines in the 8-11 μm IR. The combination of a wide spectral coverage required for backscattering analysis combined with fluorescence data, enable the generation of a large spectral data set for aerosols identification. Several natural and anthropogenic aerosol types were disseminated in controlled conditions, to test system capabilities. Reliable identification of transient and continuous phenomena demands fast and efficient control and detection algorithms. System performance, using the specially designed algorithms, is described below.

Fluid modeling covers a wide range of principles describing the motion of matter and energy in dependence on spatial scales, time scales and other attributes. In order to provide efficient numeric calculations, the information systems have to be developed for management, pre-processing, post-processing and visualization. In spite of that many software tools contain sophisticated subsystems for data management and implement advanced numerical algorithms, there is still need to standardize data inputs/outputs, wide used data analyses, and case oriented computational tools under one roof. Thus, the geographic information system (GIS) is used to satisfy all the requirements. As an example, the case study focused on dust dispersion above the surface coal mine documents the GIS ability to solve all the tasks. The input data are represented by terrain measurements of meteorological conditions and by estimates of the emission rates of potential surface dust sources. Remote sensing helps to identify and classify the coal mine surface in order to map erosion sites and other surface objects. GPS is used to improve the accuracy of the erosion site boundaries and to locate other point emission sources such as excavators, storage sites, and line emission sources such as conveyors and roads. The 3D mine surface for modeling of wind flows and dust dispersion is based on GPS measurements and laser scanning. All data inputs are integrated together with simulation outputs in the spatial database in the framework of the GIS project. In case of dispersion modeling, a few ways can be used to provide numeric calculations together with GIS analyses. The traditionally used way represents using of standalone simulation tools and the input/output data linkage through shared data files. The more advanced way is the implementation of dispersion models in the GIS environment. The methods are demonstrated by using U.S. EPA modeling tools and by linking standalone numerical calculations in the GIS environment with using case oriented programming libraries and GIS development tools.

Air density online measurement lidar paraxial imaging receiving optical system is designed according to requirements on high altitude rarefied atmosphere density real-time online measurement. Initial structure form of combining Ernostar objective lens and Tessar objective lens is adopted for the receiving objective lens in the system. Laser backward scattering optical imaging in the short-range 180 mm-3000 mm measurement area is realized under the precondition of ensuring that the receiving system has larger caliber, relative aperture and smaller volume. Aberration analysis results and test results based on ZEMAX software are provided. Results show that the imaging quality of designed objective can satisfy the requirements on online measurement of atmospheric density. Air density measurement precision with the lidar developed by the design institute is higher than 5%.