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Given the importance of constraining cloud droplet number concentrations (Nd) in low-level clouds, we explore two methods for retrieving Nd from surface-based remote sensing that emphasize the information content in lidar measurements. Because Nd is the zeroth moment of the droplet size distribution (DSD), and all remote sensing approaches respond to DSD moments that are at least 2 orders of magnitude greater than the zeroth moment, deriving Nd from remote sensing measurements has significant uncertainty. At minimum, such algorithms require the extrapolation of information from two other measurements that respond to different moments of the DSD. Lidar, for instance, is sensitive to the second moment (cross-sectional area) of the DSD, while other measures from microwave sensors respond to higher-order moments. We develop methods using a simple lidar forward model that demonstrates that the depth to the maximum in lidar-attenuated backscatter (Rmax⁡) is strongly sensitive to Nd when some measure of the liquid water content vertical profile is given or assumed. Knowledge of Rmax⁡ to within 5 m can constrain Nd to within several tens of percent. However, operational lidar networks provide vertical resolutions of > 15 m, making a direct calculation of Nd from Rmax⁡ very uncertain. Therefore, we develop a Bayesian optimal estimation algorithm that brings additional information to the inversion such as lidar-derived extinction and radar reflectivity near the cloud top. This statistical approach provides reasonable characterizations of Nd and effective radius (re) to within approximately a factor of 2 and 30 %, respectively. By comparing surface-derived cloud properties with MODIS satellite and aircraft data collected during the MARCUS and CAPRICORN II campaigns, we demonstrate the utility of the methodology. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative approach to 3D environmental mapping through the integration of a compact, handheld sensor package with a two-stage sensor fusion pipeline. The sensor package, incorporating LiDAR, IMU, RGB, and thermal cameras, enables comprehensive and robust 3D mapping of various environments. By leveraging Simultaneous Localization and Mapping (SLAM) and thermal imaging, our solution offers good performance in conditions where global positioning is unavailable and in visually degraded environments. The sensor package runs a real-time LiDAR-Inertial SLAM algorithm, generating a dense point cloud map that accurately reconstructs the geometric features of the environment. Following the acquisition of that point cloud, we post-process these data by fusing them with images from the RGB and thermal cameras and produce a detailed, color-enriched 3D map that is useful and adaptable to different mission requirements. We demonstrated our system in a variety of scenarios, from indoor to outdoor conditions, and the results showcased the effectiveness and applicability of our sensor package and fusion pipeline. This system can be applied in a wide range of applications, ranging from autonomous navigation to smart agriculture, and has the potential to make a substantial benefit across diverse fields. [ABSTRACT FROM AUTHOR]

LiDAR sensors, pivotal in various fields like agriculture and robotics for tasks such as 3D object detection and map creation, are increasingly coupled with thermal cameras to harness heat information. This combination proves particularly effective in adverse conditions like darkness and rain. Ensuring seamless fusion between the sensors necessitates precise extrinsic calibration. Our innovative calibration method leverages human presence during sensor setup movements, eliminating the reliance on dedicated calibration targets. It optimizes extrinsic parameters by employing a novel evolutionary algorithm on a specifically designed loss function that measures human alignment across modalities. Our approach showcases a notable 4.43 % improvement in the loss over extrinsic parameters obtained from target-based calibration in the FieldSAFE dataset. This advancement reduces costs related to target creation, saves time in diverse pose collection, mitigates repetitive calibration efforts amid sensor drift or setting changes, and broadens accessibility by obviating the need for specific targets. The adaptability of our method in various environments, like urban streets or expansive farm fields, stems from leveraging the ubiquitous presence of humans. Our method presents an efficient, cost-effective, and readily applicable means of extrinsic calibration, enhancing sensor fusion capabilities in the critical fields reliant on precise and robust data acquisition. [ABSTRACT FROM AUTHOR]

Self-driving vehicles demand efficient and reliable depth-sensing technologies. Lidar, with its capability for long-distance, high-precision measurement, is a crucial component in this pursuit. However, conventional mechanical scanning implementations suffer from reliability, cost, and frame rate limitations. Solid-state lidar solutions have emerged as a promising alternative, but the vast amount of photon data processed and stored using conventional direct time-of-flight (dToF) prevents long-distance sensing unless power-intensive partial histogram approaches are used. In this paper, we introduce a groundbreaking 'guided' dToF approach, harnessing external guidance from other onboard sensors to narrow down the depth search space for a power and data-efficient solution. This approach centers around a dToF sensor in which the exposed time window of independent pixels can be dynamically adjusted. We utilize a 64-by-32 macropixel dToF sensor and a pair of vision cameras to provide the guiding depth estimates. Our demonstrator captures a dynamic outdoor scene at 3 fps with distances up to 75 m. Compared to a conventional full histogram approach, on-chip data is reduced by over twenty times, while the total laser cycles in each frame are reduced by at least six times compared to any partial histogram approach. The capability of guided dToF to mitigate multipath reflections is also demonstrated. For self-driving vehicles where a wealth of sensor data is already available, guided dToF opens new possibilities for efficient solid-state lidar. [ABSTRACT FROM AUTHOR]

LiDAR and camera are pivotal sensors of 3D (three-dimensional) object detection. As a result of their different characteristics, increasingly multimodal-based object detection methods have been proposed. Now, popular methods are to hardly associate camera features with LiDAR features, but the features are frequently enhanced and aggregated, so there is a major challenge in how to align two features effectively. Therefore, we propose CAF-RCNN. On the basis of PointRCNN, using Feature Pyramid Network (FPN) to extract advanced semantic features at different scales, then fusing these features with the LiDAR features of the Set Abstraction (SA) module output in PointRCNN and subsequent steps. Regarding the features fusion module, we design a module based on the cross-attention mechanism, CAFM (Cross-Attention Fusion Module). It combines two channel attention streams in a cross-over fashion to utilize rich details about significant objects in the Image Stream and Geometric Stream. We did a lot of experiments on the KITTI dataset, and the result shows that our method is 6.43% higher than PointRCNN in 3D accuracy. [ABSTRACT FROM AUTHOR]

Over the last decade, robotic perception algorithms have significantly benefited from the rapid advances in deep learning (DL). Indeed, a significant amount of the autonomy stack of different commercial and research platforms relies on DL for situational awareness, especially vision sensors. This work explored the potential of general-purpose DL perception algorithms, specifically detection and segmentation neural networks, for processing image-like outputs of advanced lidar sensors. Rather than processing the three-dimensional point cloud data, this is, to the best of our knowledge, the first work to focus on low-resolution images with a 360° field of view obtained with lidar sensors by encoding either depth, reflectivity, or near-infrared light in the image pixels. We showed that with adequate preprocessing, general-purpose DL models can process these images, opening the door to their usage in environmental conditions where vision sensors present inherent limitations. We provided both a qualitative and quantitative analysis of the performance of a variety of neural network architectures. We believe that using DL models built for visual cameras offers significant advantages due to their much wider availability and maturity compared to point cloud-based perception. [ABSTRACT FROM AUTHOR]

Recently, 3D object detection based on multi-modal sensor fusion has been increasingly adopted in automated driving and robotics. For example, the semantic information provided by cameras and the geometric information provided by light detection and ranging (LiDAR) are fused to perceive 3D objects, as single modal sensors are unable to capture enough information from the environment. Many state-of-the-art methods fuse the signals sequentially for simplicity. By sequentially, we mean using the image semantic signals as auxiliary input for LiDAR-based object detectors would make the overall performance heavily rely on the semantic signals. Moreover, the error introduced by these signals may lead to detection errors. To remedy this dilemma, we propose an approach coined supervised-PointRendering to correct the potential errors in the image semantic segmentation results by training auxiliary tasks with fused features of the laser point geometry feature, the image semantic feature and a novel laser visibility feature. The laser visibility feature is obtained through the raycasting algorithm and is adopted to constrain the spatial distribution of fore- and background objects. Furthermore, we build an efficient anchor-free Single Stage Detector (SSD) powered by an advanced global-optimal label assignment to achieve a better time–accuracy balance. The new detection framework is evaluated on the extensively used KITTI and nuScenes datasets, manifesting the highest inference speed and at the same time outperforming most of the existing single-stage detectors with respect to the average precision. [ABSTRACT FROM AUTHOR]

The safety of vehicles is one of the major goals of driving automation. The safety distance is longer for rail vehicles such as trams because of the adherence limitations of the wheel-to-rail system. The major issues of fixed frontal sensing are fake target detection, blind spots related to rail slopes, curves, and random changes in the target's illumination or reflectivity. In this experimental study, distance measurements were performed using a scaled tram model equipped with a LiDAR sensor with a narrow field of view, under different conditions of illumination, size, and reflectivity of the target objects, and using different track configurations, to evaluate the effectiveness of such sensors in collision-avoidance systems for rail applications. The experimental findings are underlining the sensor's sensitivity to fake targets, objects in the sensor's blind spots, and special optical interferences, which are important for evaluating long-range LiDAR capabilities in sensing safety distance for vehicles. The conclusions can help developers to produce a dedicated colliding prevention system for trams and to identify the zones with high risk in the track where additional protection methods should be used. The LiDAR sensor must be used in conjunction with additional sensors to perform all the security tasks of an anti-colliding system for the tram. [ABSTRACT FROM AUTHOR]

Wheel is an important component of bogie, and its geometry affects the dynamic behaviors of rail vehicles. To accurately obtain the geometric feature information of wheel tread profile, an on-machine measurement method with the 1-D laser sensor is investigated in this article. Combined with the specific morphology of tread profile and measurement model of laser sensor, the corresponding on-machine measurement model of wheel tread profile is established according to the characteristics of inclined and curved profiles. To improve the measurement accuracy, an adaptive error correction is studied to strengthen the measurement of complex profiles and suppress the measurement error of laser sensors. And the method achieves error suppression by more than 30% in the test and calibration experiments. Finally, the measurement method based on the laser sensor is put into the practical measurement of the wheel tread profile on an underfloor wheelset lathe, and the effectiveness of the proposed method is verified with experiments. These works can not only improve the measurement accuracy of wheel geometry but also enhance the machining capability of machine tool, which is important for the safe operation of rail vehicles. [ABSTRACT FROM AUTHOR]

• A highly sensitive Airborne Laser Photo-Acoustic CH 4 Gas Analyzer (7.7 μm) is developed. • A resonant differential photo-acoustic detector and a sealed-off gas-filled Ref -cell was used. • The minimum measured background signal of the PA sensor is n min ≈ (26.6 ± 8.4) ppb CH 4 (at a bandwidth of 20 Hz). • The value of normalized noise equivalent absorption is (NNEA) = 8.22 × 10–10 cm−1·W/Hz1/2. A compact laser photo-acoustic (PA) methane sensor based on a quantum-cascade laser (∼7.7 μm; 1750 Hz; 25 mW), a resonant differential photo-acoustic detector (PAD), and a sealed-off gas-filled PA Ref -cell has been developed. Normalization of the absorption signals in the PAD is carried out according to the absorption signals in the gas-filled PA Ref -cell, which significantly reduces the measurement errors of the methane concentration in the case of instability of the laser emission wavelength. The minimum measured background signal of the PA sensor (using high purity nitrogen) is n min ≈ (26.6 ± 8.4) ppb CH 4 (at a bandwidth of 20 Hz), the value of normalized noise equivalent absorption (NNEA) = 8.22 × 10–10 cm−1·W/Hz1/2. A comparison of various research groups results with mid-IR PA gas analyzers is carried out. The developed PA methane sensor is adapted for placement on the UAV's board. Device has dimensions of 315 × 165 × 110 mm, weight ∼3.1 kg, power supply from an external source (9...60 VDC), power consumption ∼20 VA. [ABSTRACT FROM AUTHOR]

• Open-path standoff gas detection for atmospheric NO using WMS-2f/1f method and QCL. • Adaptive Kalman filter improves 33% in detection accuracy with a response time of 7 s. • Dynamic response, accuracy, sensitivity, and detection limit are evaluated in the laboratory. • Temporal evolution of NO in the atmosphere is measured by field experiments. Nitric oxide (NO) is one of the primary air pollutants, and open optical path detection of NO holds immense significance in safeguarding the ecological environment and human health. In this paper, a high precision and high sensitivity NO open optical path sensor based on mid-infrared laser absorption spectroscopy technology is developed. The average gas concentration inversion of the NO path is achieved by collecting the backscattered light from the cooperative target. The gas measurement deploys a wavelength modulation method to remove the effects from light intensity variations. Furthermore, a Kalman filter and a temperature–pressure sensor optimization software algorithm were utilized to reduce the effects of temperature–pressure variations during the measurement process, leading to a 33% improvement in detection accuracy. The performance of the sensor was evaluated in the laboratory environment in terms of accuracy, sensitivity, and detection limit. The atmospheric NO concentration was also measured under varying time periods and weather conditions in the field environment, thus laying a strong foundation for studying the spatial and temporal evolution of NO in the atmosphere. [ABSTRACT FROM AUTHOR]

The flatness error is one of the critical parameters of a circular saw, which seriously affects the cutting performance. In this paper, the measurement of the circular saw flatness error based on a laser displacement sensor is proposed. For the tooth of a circular saw, the plane is discontinuous. According to the field detection demand of a circular saw, the authors collected Archimedean spiral three coordinate data of the circular saw using a laser displacement sensor. Considering the practical application of circular saw flatness detection, the authors have established an ideal plane model of the flatness based on the least‐squares method to evaluate the flatness error of circular saws. The system has a maximum detection area of 400 mm × 400 mm, the measurement time is 80 s, and the measurement absolution accuracy is 10 μm. [ABSTRACT FROM AUTHOR]

A new method of measuring the static deformation of rotating blades based on BTT is proposed and examined in experiments. To achieve high accuracy and robustness, the method utilizes coordinates on both the suction surface and the pressure surface from multiple probes and fits them to the complete tip profile. To realize this, a backward fitting algorithm is designed which solves the deformation parameters in a coupled fashion. Hardware advancement is also implemented where the departure time instants besides the arrival time instants of each blade are collected. A novel validation method using the laser displacement sensor is also presented to provide a benchmark. In the laboratory experiment, it is found that the new method could reduce the calibration uncertainty to 43%–61% by taking the suction surface into account. Compared to the existing methods, the measurement error of the new method is also the smallest. The proposed method has been validated on a single-rotor compressor rig. • A new method (FullProFit) to measure the static deformation of rotating blades. • Utilization of the measured coordinates on the suction surface. • Using backward fitting to incorporate multiple samples and elevate efficiency. • Direct experimental validation based on the laser displacement sensor. [ABSTRACT FROM AUTHOR]

• A high-sensitive multi-pass photoacoustic spectroscopy sensor is developed for hydrogen sulfide. • With the enhancement of effective laser power in the multi-pass PA cell, the 2 f -signal of the target H 2 S line has a ∼ 8 times improvement. • The minimum detection concentration of the system can reach 0.13 ppm with a 1-minute averaging time. A high-sensitive tunable diode laser sensor based on photoacoustic spectroscopy and multi-pass absorption spectroscopy is developed for hydrogen sulfide (H 2 S) detection using a target absorption line at 6287.005 cm−1. Two spherical concave mirrors are utilized as the reflectors to make the laser beam pass through the photoacoustic cell for 24 times. The wavelength modulation spectroscopy technique with second harmonic detection (WMS- 2f) method is employed to reduce external interference and increase the detection sensitivity. With the enhancement of the effective laser power in the multi-pass PA cell, the photoacoustic 2f signal of the target H 2 S absorption line has a ∼ 8 times improvement compared to the sensor system without multi-pass cell. Based on the results of Allan variance analysis, the minimum detection concentration of the system could reach 0.68 ppm with a 10-s average time and it can be further lowered down to ∼ 0.1 ppm when a 1-minute averaging time is used. The linear responsivity of the multi-pass PA spectroscopy sensor is estimated to be ∼ 0.00112 mV/ppm by linear fitting for a continuous flow gas measurement results. The excellent performance validates the potential of the sensor for field application. [ABSTRACT FROM AUTHOR]

When using single-photon detector array lidar to detect ships in a foggy environment, the multiple scattering between laser and sea fog, sea surface and ships leads to a large number of fake images in the reconstructed depth images, which seriously limits the ability to detect and recognize ships. Here, we propose a three-dimensional imaging algorithm suitable for ships in fog with a single-photon detector array lidar. This algorithm can eliminate fake images caused by multiple reflections of the sea surface in a foggy environment. Firstly, the algorithm uses a dual-Gamma estimation algorithm to eliminate the influence of non-signal photons, and uses a pulse-like selection algorithm to accurately extract the target echo waveform. Secondly, based on the characteristics of the target echo, the principal component analysis (PCA) and K-means algorithm are used to cluster the features of the separated echo signals. Finally, the result of feature classification is used to achieve efficient fake images removal. We show that the proposed algorithm can more accurately reconstruct the depth image of a ship with a distance of 1.72 km in fog environment. Compared with the traditional algorithms, the proposed algorithm can suppress the sea surface reflection while retaining the most target contour. It has been successfully demonstrated for small ships at different distances. This research is expected to improve the detection and recognition ability of single-photon detector array lidar for small targets in complex scenes. [ABSTRACT FROM AUTHOR]

In article number 2100511, Nanxi Li and colleagues present a comprehensive review on recent advances in solid-state LiDAR and nanophotonics-based LiDAR sensors. B Light Detection and Ranging (LiDAR) Sensors b Light detection and ranging (LiDAR) sensors enable precision sensing of an object in 3D. Also, LiDAR sensor principles are introduced, and future outlook on advanced LiDAR sensors is discussed. [Extracted from the article]

With the continuous improvement of the theory and method of gear measurement, the effectiveness of gear contact measurement is reduced, making it difficult to meet the need for 100% gear detection. To improve the measurement efficiency and make effective use of the full tooth surface data, a gear pitch rapid measurement method by point laser sensor is proposed. This method studies the principle of measuring the gear based on a laser point sensor, and the efficient model of collection and analysis of tooth surface data is established. Then the evaluation of the pitch deviation is completed, including the individual single pitch deviation, the single pitch deviation, the individual cumulative pitch deviation, and the total cumulative pitch deviation. Additionally, the pitch deviation of the multiple cross-sections is investigated, which may characterize the overall pitch deviation of the tooth surface. Compared to the gear measuring center P26, the experimental results show that the optical measurement of pitch deviation is feasible and efficient. Depending on the different measuring conditions, the offset status of the sensor can be selected to meet the different efficiency and accuracy requirements. The research and application potential of a point laser sensor in gear measurement is wide-ranging. [ABSTRACT FROM AUTHOR]