Light Detection And Ranging Sensors Research Articles

Cumulative error correction of inertial navigation systems using LIDAR sensors and extended Kalman filter

Autonomous robots have gained significant attention in research due to their ability to facilitate human work. Navigation systems, particularly localization, present a challenge in autonomous robots. The inertial navigation system is a localization system that uses inertial sensors and a wheel odometer to estimate the robot’s relative position to the initial position. However, the system is susceptible to continuous error accumulation over time due to factors like sensor noise and wheel slip. To address these issues, external sensors are required to measure the robot’s position in the environment. The extended Kalman filter (EKF) method is utilized to estimate the robot’s position based on wheel odometer and light detection and ranging (LIDAR) sensor measurements. In the prediction stage, the input to the EKF is the position measurement from the wheel odometer, while the LIDAR sensor’s position measurement is used in the update stage to improve the prediction stage results. The test results reveal that the EKF’s estimated position has a lower average error compared to the position measurement using the wheel odometer. Therefore, it can be concluded that the EKF technique is effectively applied to the robot and can correct the wheel odometer's cumulative error with the assistance of the LIDAR sensor.

Developing a prediction model in a lightweight packaging waste sorting plant using sensor-based sorting data combined with data of external near-infrared and LiDAR sensors.

Sensor-based material flow monitoring allows for continuously high output qualities, through quality management and process control. The implementation in the waste management sector, however, is inhibited by the heterogeneity of waste and throughput fluctuations. In this study, challenges and possibilities of using different types of sensors in a lightweight packaging waste sorting plant are investigated. Three external sensors have been mounted on different positions in an Austrian sorting plant: one Light Detection and Ranging (LiDAR) sensor for monitoring the volume flow and two near-infrared (NIR) sensors for measuring the pixel-based material composition and occupation density. Additionally, the data of an existing sensor-based sorter (SBS) were evaluated. To predict the newly introduced parameter material-specific occupation density (MSOD) of multi-coloured polyethylene terephthalate (PET) preconcentrate, different machine learning models were evaluated. The results indicate that using SBS data for both monitoring of throughput fluctuations caused by different bag opener settings as well as monitoring the material composition is feasible, if the pre-set teach-in is suitable. The ridge regression model based on SBS was comparable (RMSE = 3.59 px%, R² = 0.57) to the one based on NIR and LiDAR (RMSE = 3.1 px%, R² = 0.68). The demonstrated feasibility of the implementation at plant scale highlights the opportunities of sensor-based material flow monitoring for the waste management sector and paves the way towards a more circular plastics economy.

Dynamic Occupancy Grid Map with Semantic Information Using Deep Learning-Based BEVFusion Method with Camera and LiDAR Fusion

In the field of robotics and autonomous driving, dynamic occupancy grid maps (DOGMs) are typically used to represent the position and velocity information of objects. Although three-dimensional light detection and ranging (LiDAR) sensor-based DOGMs have been actively researched, they have limitations, as they cannot classify types of objects. Therefore, in this study, a deep learning-based camera–LiDAR sensor fusion technique is employed as input to DOGMs. Consequently, not only the position and velocity information of objects but also their class information can be updated, expanding the application areas of DOGMs. Moreover, unclassified LiDAR point measurements contribute to the formation of a map of the surrounding environment, improving the reliability of perception by registering objects that were not classified by deep learning. To achieve this, we developed update rules on the basis of the Dempster–Shafer evidence theory, incorporating class information and the uncertainty of objects occupying grid cells. Furthermore, we analyzed the accuracy of the velocity estimation using two update models. One assigns the occupancy probability only to the edges of the oriented bounding box, whereas the other assigns the occupancy probability to the entire area of the box. The performance of the developed perception technique is evaluated using the public nuScenes dataset. The developed DOGM with object class information will help autonomous vehicles to navigate in complex urban driving environments by providing them with rich information, such as the class and velocity of nearby obstacles.

ZUST Campus: A Lightweight and Practical LiDAR SLAM Dataset for Autonomous Driving Scenarios

This research proposes a lightweight and applicable dataset with a precise elevation ground truth and extrinsic calibration toward the LiDAR (Light Detection and Ranging) SLAM (Simultaneous Localization and Mapping) task in the field of autonomous driving. Our dataset focuses on more cost-effective platforms with limited computational power and low-resolution three-dimensional LiDAR sensors (16-beam LiDAR), and fills the gaps in the existing literature. Our data include abundant scenarios that include degenerated environments, dynamic objects, and large slope terrain to facilitate the investigation of the performance of the SLAM system. We provided the ground truth pose from RTK-GPS and carefully rectified its elevation errors, and designed an extra method to evaluate the vertical drift. The module for calibrating the LiDAR and IMU was also enhanced to ensure the precision of point cloud data. The reliability and applicability of the dataset are fully tested through a series of experiments using several state-of-the-art LiDAR SLAM methods.

Multispectral Light Detection and Ranging Technology and Applications: A Review.

Light Detection and Ranging (LiDAR) is a well-established active technology for the direct acquisition of 3D data. In recent years, the geometric information collected by LiDAR sensors has been widely combined with optical images to provide supplementary spectral information to achieve more precise results in diverse remote sensing applications. The emergence of active Multispectral LiDAR (MSL) systems, which operate on different wavelengths, has recently been revolutionizing the simultaneous acquisition of height and intensity information. So far, MSL technology has been successfully applied for fine-scale mapping in various domains. However, a comprehensive review of this modern technology is currently lacking. Hence, this study presents an exhaustive overview of the current state-of-the-art in MSL systems by reviewing the latest technologies for MSL data acquisition. Moreover, the paper reports an in-depth analysis of the diverse applications of MSL, spanning across fields of "ecology and forestry", "objects and Land Use Land Cover (LULC) classification", "change detection", "bathymetry", "topographic mapping", "archaeology and geology", and "navigation". Our systematic review uncovers the potentials, opportunities, and challenges of the recently emerged MSL systems, which integrate spatial-spectral data and unlock the capability for precise multi-dimensional (nD) mapping using only a single-data source.

A Novel Approach to Light Detection and Ranging Sensor Placement for Autonomous Driving Vehicles Using Deep Deterministic Policy Gradient Algorithm

<div>This article presents a novel approach to optimize the placement of light detection and ranging (LiDAR) sensors in autonomous driving vehicles using machine learning. As autonomous driving technology advances, LiDAR sensors play a crucial role in providing accurate collision data for environmental perception. The proposed method employs the deep deterministic policy gradient (DDPG) algorithm, which takes the vehicle’s surface geometry as input and generates optimized 3D sensor positions with predicted high visibility. Through extensive experiments on various vehicle shapes and a rectangular cuboid, the effectiveness and adaptability of the proposed method are demonstrated. Importantly, the trained network can efficiently evaluate new vehicle shapes without the need for re-optimization, representing a significant improvement over classical methods such as genetic algorithms. By leveraging machine learning techniques, this research streamlines the sensor placement optimization process, enhancing the perception capabilities of autonomous driving vehicles. The optimized sensor configurations obtained from the DDPG algorithm lead to safer and more reliable autonomous driving systems, contributing to the advancement and widespread adoption of autonomous driving technology.</div>

MARS-LVIG dataset: A multi-sensor aerial robots SLAM dataset for LiDAR-visual-inertial-GNSS fusion

In recent years, advancements in Light Detection and Ranging (LiDAR) technology have made 3D LiDAR sensors more compact, lightweight, and affordable. This progress has spurred interest in integrating LiDAR with sensors such as Inertial Measurement Units (IMUs) and cameras for Simultaneous Localization and Mapping (SLAM) research. Public datasets covering different scenarios, platforms, and viewpoints are crucial for multi-sensor fusion SLAM studies, yet most focus on handheld or vehicle-mounted devices with front or 360-degree views. Data from aerial vehicles with downward-looking views is scarce, existing relevant datasets usually feature low altitudes and are mostly limited to small campus environments. To fill this gap, we introduce the Multi-sensor Aerial Robots SLAM dataset (MARS-LVIG dataset), providing unique aerial downward-looking LiDAR-Visual-Inertial-GNSS data with viewpoints from altitudes between 80 m and 130 m. The dataset not only offers new aspects to test and evaluate existing SLAM algorithms, but also brings new challenges which can facilitate researches and developments of more advanced SLAM algorithms. The MARS-LVIG dataset contains 21 sequences, acquired across diversified large-area environments including an aero-model airfield, an island, a rural town, and a valley. Within these sequences, the UAV has speeds varying from 3 m/s to 12 m/s, a scanning area reaching up to 577,000 m2, and the max path length of 7.148 km in a single flight. This dataset encapsulates data collected by a lightweight, hardware-synchronized sensor package that includes a solid-state 3D LiDAR, a global-shutter RGB camera, IMUs, and a raw message receiver of the Global Navigation Satellite System (GNSS). For algorithm evaluation, this dataset releases ground truth of both localization and mapping, which are acquired by on-board Real-time Kinematic (RTK) and DJI L1 (post-processed by its supporting software DJI Terra), respectively. The dataset can be downloaded from: https://mars.hku.hk/dataset.html .

Digital twinning of surveillance robot

Surveillance robots provide troops with real-time information about their surroundings, including enemy positions, terrain, and potential threats. This information is invaluable for making informed decisions and ensuring the safety of military personnel. Geo-fenced robots are robots equipped with technology that restricts their movements within predefined geographic boundaries. These boundaries are typically established using GPS or other location-based technologies. In applications like the military, geo-fencing can be used to establish secure zones. Geo-fencing helps prevent robots from entering hazardous areas, reducing the risk of damage to the robot and potential harm to friendly forces or civilians This project aims at Digital twinning of robots by creating a virtual replica or model of a physical robot in a digital environment. Digital twinning allows engineers and designers to simulate and test the robot’s behavior, performance, and capabilities in a virtual environment before building the physical robot. This can lead to increased efficiency, reduced downtime, and cost savings. In this project, LiDAR (Light Detection and Ranging) sensor data is integrated with a digital twinned robot to create a virtual reproduction of the robot and its surroundings. LiDAR is a remote sensing technology that maps the robot’s surroundings in fine detail using 3D point clouds by measuring distances using laser pulses. Here we make use of RPLIDAR A1 M8 and acquire data from it using ROS with the help of a RaspberryPi4B Controller. Simulink is used to create a 3D model of the robot’s environment and the robot itself. Reinforcement learning and pure pursuit algorithms are used for developing them. This project discusses the need for geofenced autonomous robots and emphasizes the security and reliability it brings to military applications.

Hostage Liberation Operations using Wheeled Robots Based on LIDAR (Light Detection and Ranging) Sensors

Hostage release operations require a high level of precision, alertness, and skill, which are carried out manually by soldiers of the Indonesian National Army. This medium presents a significant risk to soldiers. This research aims to improve the effectiveness of hostage release operations by integrating wheeled robot technology based on Light Detection and Ranging (LIDAR) sensors. The research method used is experiment-based in developing and testing a prototype of a mobile robot equipped with LIDAR technology and a web camera capable of mapping the location of hostages in three dimensions. The research showed that this robot has high accuracy, reaching 97.87%, and can createthree-dimensional route maps and display real-time video on a computer. The use of this technology has the potential to reduce risks to soldiers and improve the accuracy of mapping hostage locations, which can ultimately improve the safety and effectiveness of hostage release operations in the context of special operations tasks by soldiers of the Army.

Moving event detection from LiDAR point streams

In dynamic environments, robots require instantaneous detection of moving events with microseconds of latency. This task, known as moving event detection, is typically achieved using event cameras. While light detection and ranging (LiDAR) sensors are essential for robots due to their dense and accurate depth measurements, their use in event detection has not been thoroughly explored. Current approaches involve accumulating LiDAR points into frames and detecting object-level motions, resulting in a latency of tens to hundreds of milliseconds. We present a different approach called M-detector, which determines if a point is moving immediately after its arrival, resulting in a point-by-point detection with a latency of just several microseconds. M-detector is designed based on occlusion principles and can be used in different environments with various types of LiDAR sensors. Our experiments demonstrate the effectiveness of M-detector on various datasets and applications, showcasing its superior accuracy, computational efficiency, detection latency, and generalization ability.

Implementation of LiDAR Sensor for Mobile Robot Delivery Based on Robot Operating System

Recent research has focused on the development of mobile delivery robots as part of the rapidly evolving autonomous technology. The main focus is on creating mobile robots capable of efficiently delivering goods without human intervention. This involves designing systems, core technologies, and overcoming various challenges. The navigation system of the delivery robot is enhanced by the use of LIDAR (Light Detection and Ranging) sensors. One of the sensors used is the RPLIDAR A1M8 which is capable of detecting nearby objects with a distance reading of 360°. Trials were conducted to evaluate the robot's navigation performance with a load variation of 0 kg to 20 kg. The test results show that RPLIDAR A1M8 can read distances up to 1200 cm with an error rate of about 3.15%, and a minimum distance of 15 cm with an error of 1.45%. The durability of this sensor reading is well maintained, with a presentation error of only 0.4%. Mapping trials in the room produced an average error of 18.57% without tracking, and 5.02% with tracking. The weight of the load carried by the robot affects the speed and travel time. The heavier the load, the robot speed decreases from 0.264 m/s at 0 kg load to 0.127 m/s at 20 kg load. Likewise, the travel time increases with the weight of the load, from 7.6 seconds to 15.6 seconds.

A 12-bit, 27.8-ps resolution, Anti-PVT variation multi-parallel sampling based coarse-fine TDC for LiDAR sensors

This paper presents a multi-parallel sampling based coarse-fine time-to-digital converter (TDC) with sub-gate delay resolution, large measurement range and high conversion rate for light detection and ranging (LiDAR) sensors. The fine-TDC is designed based on a voltage-controlled delay line (VCDL) but with multi-parallel sampling D flip-flop (DFF) groups to achieve a sub-gate delay resolution based on multi-phase interpolation. These DFF groups are triggered by several successively delayed sampling clocks that generated by a phase-locked loop (PLL) based multi-phase clock generator. The coarse-TDC composed of a coarse counter and a logical control circuit (LCC) is designed to enlarge the measurement range. The LCC is used to calibrate and guarantee the coarse counter neither over-counted nor under-counted. Since the delay of delay units in fine-TDC is also voltage-controlled by another PLL, the resolution of this proposed TDC is robust to process-voltage-temperature (PVT) variation. Besides, this proposed TDC architecture also has the property of dynamic element matching (DEM), which contributes to mitigate the impact of device mismatch and scramble the quantization noise. The post-layout simulations demonstrate a 12-bit, 27.8-ps time resolution, 0.86-LSB DNL, 0.92-LSB INL with 13.6-mW power consumption at 33.3-MS/s under a 1.8-V supply.

Investigation of geometric object and indoor mapping capacity of Apple iPhone 12 Pro LiDAR

LiDAR (light detection and ranging) sensors use laser beams to calculate distances in the surroundings. These sensors can be applied to a wide range of tasks, and they are frequently helpful in tasks like building 3D maps, navigating airplanes, robots, conducting mining operations, and automated driving. High-resolution distance measurements are taken by LiDAR sensors, but they also gather environmental data. This information aids in locating, identifying, and quantifying things and their surroundings. The iPhone 12 Pro, which Apple released in 2020, was evaluated for accuracy with various geometric shapes and its capacity to recognize indoor environments. Free of charge 3D Scanner and the Clirio Scan application were employed in this situation. However, it was found that the root mean square error and mean error in indoor mapping were ±1.41 cm and -0.56 cm in 3D Scanner and ±3.94 cm and -0.60 cm in the Clirio Scan application, respectively, despite the findings obtained showing low accuracy in scanning small geometric objects due to the scanning difficulty. Clirio does not reject the null hypothesis in the t-test that was conducted. The accuracy of the LiDAR sensor in indoor mapping has been shown to be more promising than that of small items. In order to evaluate the reliability and reusability of the indoor mapping application according to reference measurements, intraclass correlation test was performed and the results were determined to be reliable.

DEEP LEARNING FOR OBJECT DETECTION USING RADAR DATA

Abstract. Recently, Deep learning algorithms are becoming increasingly instrumental in autonomous driving by identifying and acknowledging road entities to ensure secure navigation and decision-making. Autonomous car datasets play a vital role in developing and evaluating perception systems. Nevertheless, the majority of current datasets are acquired using Light Detection and Ranging (LiDAR) and camera sensors. Utilizing deep neural networks yields remarkable outcomes in object recognition, especially when applied to analyze data from cameras and LiDAR sensors which perform poorly under adverse weather conditions such as rain, fog, and snow due to the sensor wavelengths. This paper aims to evaluate the ability to use RADAR dataset for detecting objects in adverse weather conditions, when LiDAR and Cameras may fail to be effective. This paper presents two experiments for object detection using Faster-RCNN architecture with Resnet-50 backbone and COCO evaluation metrics. Experiment 1 is object detection over only one class, while Experiment 2 is object detection over eight classes. The results show that as expected the average precision (AP) of detecting one class is (47.2) which is better than the results from detecting eight classes (27.4). Comparing my results from experiment 1 to the literature results which achieved an overall AP (45.77), my result was slightly better in accuracy than the literature mainly due to hyper-parameters optimization. The outcomes of object detection and recognition based on RADAR indicate the potential effectiveness of RADAR data in automotive applications particularly in adverse weather conditions, where vision and LiDAR may encounter limitations.

Bridge deck surface damage assessment using point cloud data

Bridge deck condition assessments are typically conducted through visual inspections and by incorporating traditional contact sensors for Non-Destructive Evaluation techniques such as hammer sounding and chain dragging, which require the keen expertise of trained inspectors. The accuracy of these inspections is proportional to the level of deterioration of the bridge deck, as the ability of the inspectors is correlated to the apparent level of damage. This study aims to improve the accuracy of bridge deck inspection processes by utilizing non-destructive evaluation techniques, including analyzing point cloud data gathered via Light Detection and Ranging (LiDAR) as a geometry-capturing tool for identifying surface irregularities. This research aims to evaluate and quantify the effectiveness and efficiency of LiDAR sensors in contributing to the suite of technologies available to perform bridge deck condition assessment. To achieve this, the research proposes to understand the deterioration pattern of New Jersey bridges, evaluate the results gathered from point cloud data collected on a full-scale bridge deck, and quantify the information gained from deploying LiDAR on operating bridges in New Jersey. Two data processing approaches were chosen to measure the gross and fine dimensions of the evaluated bridge decks, such as the Curvature Extraction and Slope Analysis method, and the Least Square Plane Fitting method, resulting in an accuracy of 97.92% in reference to the results gathered from reports generated through the analysis of state-of-the-art NDE technology data and visual inspection.

Velocity Estimation from LiDAR Sensors Motion Distortion Effect

Many modern automated vehicle sensor systems use light detection and ranging (LiDAR) sensors. The prevailing technology is scanning LiDAR, where a collimated laser beam illuminates objects sequentially point-by-point to capture 3D range data. In current systems, the point clouds from the LiDAR sensors are mainly used for object detection. To estimate the velocity of an object of interest (OoI) in the point cloud, the tracking of the object or sensor data fusion is needed. Scanning LiDAR sensors show the motion distortion effect, which occurs when objects have a relative velocity to the sensor. Often, this effect is filtered, by using sensor data fusion, to use an undistorted point cloud for object detection. In this study, we developed a method using an artificial neural network to estimate an object’s velocity and direction of motion in the sensor’s field of view (FoV) based on the motion distortion effect without any sensor data fusion. This network was trained and evaluated with a synthetic dataset featuring the motion distortion effect. With the method presented in this paper, one can estimate the velocity and direction of an OoI that moves independently from the sensor from a single point cloud using only one single sensor. The method achieves a root mean squared error (RMSE) of 0.1187 m s−1 and a two-sigma confidence interval of [−0.0008 m s−1, 0.0017 m s−1] for the axis-wise estimation of an object’s relative velocity, and an RMSE of 0.0815 m s−1 and a two-sigma confidence interval of [0.0138 m s−1, 0.0170 m s−1] for the estimation of the resultant velocity. The extracted velocity information (4D-LiDAR) is available for motion prediction and object tracking and can lead to more reliable velocity data due to more redundancy for sensor data fusion.

A multi-sensor fusion framework with tight coupling for precise positioning and optimization

In the dynamic landscape of artificial intelligence and robotics, the pursuit of accurate positioning in mobile robots has intensified. This research addresses the limitations of single-sensor SLAM (Simultaneous Localization and Mapping) techniques in complex settings by harnessing the collective strengths of LiDAR (Light Detection And Ranging), Camera, IMU (Inertial Measurement Unit), and GNSS (Global Navigation Satellite System) sensors. The proposed multi-sensor tightly-coupled SLAM framework is an integration of point-line feature-based laser–visual–inertial odometry, visual–laser fusion loop closure detection, and factor graph-based back-end optimization. Within the visual–inertial subsystem, an advanced LSD (Line Segment Detector) feature extraction strategy is introduced, incorporating point-line fusion to enhance visual line features. Additionally, the laser point cloud is projected onto the camera coordinate system, establishing depth associations with visual attributes. Strengthening the robustness of the visual–inertial subsystem in low-texture environments, camera poses undergo optimization through a sliding-window bundle adjustment method. In the laser-inertial subsystem, IMU preintegration mitigates laser point cloud distortion. Extracting edge and plane features, coupled with frame-to-local-map matching, enhances matching efficiency while streamlining computational intricacies. This amalgamation forms the basis of the laser–visual–inertial odometry fusion system. To overcome the limitations of standalone visual and laser-based loop closure detection, a dual-loop closure method utilizing visual–laser fusion is proposed. Leveraging the DBoW2 bag-of-words model, complemented by temporal–spatial consistency checks, enhances detection efficiency and accuracy. The integration of GNSS factors imparts global constraints for expansive outdoor scenarios. Employing factor graph-based back-end optimization, the refinement of laser–visual–inertial odometry factors, visual–inertial odometry factors, IMU preintegration factors, loop closure factors, and GNSS factors culminates in precise global pose estimation and high-fidelity point cloud maps. Through rigorous evaluation of the M2DGR dataset and a mobile robot platform, the proposed methodology emerges as an exemplar of performance, showcasing superiority over the state-of-the-art LIO-SAM technique. Achieving a reduction of 2.86m and 3.23m in the root mean square error of absolute pose estimation across divergent environments, this approach exhibits remarkable efficacy in outdoor scenarios, thereby elevating the precision and resilience of SLAM algorithms for mobile robots.

Automated Method for SLAM Evaluation in GNSS-Denied Areas

The automated inspection and mapping of engineering structures are mainly based on photogrammetry and laser scanning. Mobile robotic platforms like unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), but also handheld platforms, allow efficient automated mapping. Engineering structures like bridges shadow global navigation satellite system (GNSS), which complicates precise localization. Simultaneous localization and mapping (SLAM) algorithms offer a sufficient solution, since they do not require GNSS. However, testing and comparing SLAM algorithms in GNSS-denied areas is difficult due to missing ground truth data. This work presents an approach to measuring the performance of SLAM in indoor and outdoor GNSS-denied areas using a terrestrial scanner Leica RTC360 and a tachymeter to acquire point cloud and trajectory information. The proposed method is independent of time synchronization between robot and tachymeter and also works on sparse SLAM point clouds. For the evaluation of the proposed method, three LiDAR-based SLAM algorithms called KISS-ICP, SC-LIO-SAM, and MA-LIO are tested using a UGV equipped with two light detection and ranging (LiDAR) sensors and an inertial measurement unit (IMU). KISS-ICP is based solely on a single LiDAR scanner and SC-LIO-SAM also uses an IMU. MA-LIO, which allows multiple (different) LiDAR sensors, is tested on a horizontal and vertical one and an IMU. Time synchronization between the tachymeter and SLAM data during post-processing allows calculating the root mean square (RMS) absolute trajectory error, mean relative trajectory error, and the mean point cloud to reference point cloud distance. It shows that the proposed method is an efficient approach to measure the performance of SLAM in GNSS-denied areas. Additionally, the method shows the superior performance of MA-LIO in four of six test tracks with 5 to 7 cm RMS trajectory error, followed by SC-LIO-SAM and KISS-ICP in last place. SC-LIO-SAM reaches the lowest point cloud to reference point cloud distance in four of six test tracks, with 4 to 12 cm.

EVALUATION OF CONSUMER-GRADE AND SURVEY-GRADE UAV-LIDAR

Abstract. Driven by developments in the automotive industry, the availability of compact consumer-grade LiDAR (Light Detection and Ranging) sensors has increased significantly in recent years. Some of these sensors are also suitable for UAV-based surveying tasks. This paper first discusses the differences between consumer-grade and survey-grade LiDAR systems. Special attention will be paid to the scanning mechanisms used on the one hand and to different solutions for the transceiver units on the other hand. Based on the technical data of two concrete systems, the consumer-grade DJI Zenmuse L1 sensor and the survey-grade scanner RIEGL VUX-1UAV, the expected effects of the sensor parameters on the 3D point cloud are first discussed theoretically and then verified using an exemplary data set in Hessigheim (Baden-Württemberg, Germany). The analysis shows the possibilities and limitations of consumer-grade LiDAR. Compared to the low-cost sensor, the high-end scanner exhibits lower range measurement noise (5–10 mm) and better 3D point location accuracy. Furthermore, the higher laser beam quality of high-end devices (beam divergence, beam shape) enables more detailed object detection at the same point density. With moderate accuracy requirements of 5–10 cm, however, applications in the geodetic-cartographic context also arise for the considerably less expensive consumer-grade LiDAR systems.

Water level identification with laser sensors, inertial units, and machine learning

Flood risk management usually hinges on accurate water level identification in urban streams such as rivers or creeks. Although research has emphasised the applicability of ultrasonic sensors as a contactless technology for sensor-based water level monitoring, Light Detection and Ranging (LiDAR) sensors are less sensitive to weather conditions that typically happen during flood events, such as dust, fog and rainfall. However, there has been little research on the applicability of LiDAR sensors in this field. No previous literature has analysed the impact of complicating variables on the quality of predictions or evaluated the possible benefits of using a combined approach with Inertial Measurement Units (IMU) and machine learning to produce superior predictions. In this work, we collected a dataset in a laboratory condition synchronising data from a LiDAR, an ultrasonic sensor and an IMU in an experimental device. We controlled the incidence angle, the distance, and the water turbidity to analyse their effect on the predictions. Traditional machine-learning techniques were evaluated as models to combine data from distance and inertial sensors, reducing the error rates compared to individual sensors’ predictions. Results indicated a sharp drop in the mean absolute error, root mean squared error and coefficient of determination for all water turbidity and incidence angles considered, especially when tree-based ensembles were used. The ultrasonic sensor led to improved results for low water turbidity and increased incidence angle, but statistically significant differences were not found in the other cases.