Lidar Accuracy Research Articles

Strong Robust LIO System Based on Event Camera Assistance

Abstract. At present, the LiDAR system can achieve high precision and is extensively used for indoor and outdoor mobile positioning and mapping. However, LiDAR systems still face issues in cluttered environments where strong features are absent, leading to a degradation of the LiDAR-based solution. When the carrier movement involves high-speed or prolonged exposure to the mirror wall, it can cause severe degradation issues or even positioning failures in the laser slam system. Event cameras are vision sensors inspired by biology that exhibit strong robustness in high dynamic and low texture environments, potentially leading to better performance in such environments. However, some issues with event cameras remain unresolved. In this paper, a multi-source fusion method based on EVIO, LIO and IMU trajectory layer post-processing method is proposed, which will fully consider the robustness of event camera in high dynamic environment and the high precision advantage of Lidar in conventional environment, and use an algorithm based on normalized uncertainty. The elastic multi-source fusion of event camera and LiDAR is realized and tested in realde environment. Experimental results show that the proposed algorithm can effectively improve the accuracy of event camera and LiDAR. Compared to the current more advanced algorithms, the proposed algorithm effectively addresses the problem of LIO in degraded environments. Additionally, it mitigates the scale inaccuracy and divergence of the EVIO trajectory to some extent. Compared to LIO, the algorithm can reduce the maximum position error by approximately 30% and increase the overall position accuracy by 32%. Additionally, it can significantly constrain the divergence of errors in the Y direction, improving its accuracy by about 75% and 65% compared to the LIO and EVIO algorithms, respectively.

Fast automatic docking control for electric tractors

In this study, we investigate the automatic docking of an electric tractor with a semi-trailer in narrow and long-distance scenarios. We propose an automatic docking method based on a multi-sensor fusion high-precision positioning algorithm and a multi-strategy docking control algorithm. To solve the heading-angle error of the involved camera due to mechanical errors, a compensation value in the form of angle deviation is introduced. To alleviate the increase in the camera detection error with longitudinal distance, a piecewise coordinate compensation function is proposed. Repositioning is performed using light detection and ranging (Lidar) to improve the pose-perception accuracy to the millimeter level. To avoid the interference of point clouds on the relocation accuracy of Lidar, a search-and-location method that combines both small and large neighborhoods is proposed. To achieve the desired docking success rate, a multi-strategy-adjustment control algorithm is used to enable comprehensive adjustment, including approximate control of the maximum angle based on lateral deviation and fine proportional-integral-differential (PID) control based on lateral deviation and longitudinal distance, security check cycle and premature warning mechanism, and risk avoidance control. Finally, the reliability and stability of the design scheme are verified by conducting multiple experiments involving actual vehicles.

Comparative Analysis of LiDAR and CCTV Sensor Accuracy at Signalized Intersections Under Varied Weather Conditions

Comparative Analysis of LiDAR and CCTV Sensor Accuracy at Signalized Intersections Under Varied Weather Conditions

Comparative analysis of sandy beach and foredune geomorphic change measurements from Apple lidar and small-unoccupied aerial systems

Sandy beach and foredune environments are common throughout coastlines globally. Coastal landscapes are dynamic and vulnerable to water level fluctuations, storm events, and human disturbances. Standard methods for measuring geomorphic changes include small-unoccupied aircraft systems paired with structure-from-motion photogrammetry (sUAS-SfM), but this can be costly and logistically challenging. We evaluated the accuracy of Apple lidar in comparison to high precision sUAS-SfM and RTK-GPS to map sandy beach and foredune geomorphic change. Checkpoint elevations were measured via RTK-GPS and both the sUAS-SfM and Apple lidar surveyed elevations were compared against these checkpoints to evaluate the performance of both methods for measuring elevations. The sUAS-SfM elevation data were on average around 0.004 m above/below the checkpoint elevations while the Apple Lidar elevations were around 0.039 m. Apple lidar and sUAS-SfM-derived volumetric measurements and spatial patterns of erosion and accretion were compared to evaluate the Apple lidar’s ability to detect geomorphic change over time. The geomorphic changes documented from these two methods were similar though the Apple lidar appeared to capture finer-scale erosion and accretion patterns. Our findings indicate that the Apple lidar can capture sandy beach and foredune geomorphic changes rapidly and accurately, which can promote proactive and resilient coastal management.

Canopy Laser Interception Compensation Mechanism—UAV LiDAR Precise Monitoring Method for Cotton Height

Plant height is a crucial phenotypic trait that plays a vital role in predicting cotton growth and yield, as well as in estimating biomass in cotton plants. The accurate estimation of canopy height from single-flight LiDAR data remains a formidable challenge in current high-density cotton cultivation patterns, where dense foliage obstructs the collection of bare soil terrain, particularly after flowering. The existing LiDAR-based methods for cotton height estimation suffer from significant errors. In this study, a new method is proposed to compensate for the canopy height estimation by using the canopy laser interception rate. The ground points are extracted by the ground filtering algorithm, and the interception rate of the laser per unit volume of the canopy is calculated to represent the canopy density and compensate for the cotton height estimation. The appropriate segmented height compensation function is determined by grouping and step-by-step analysis of the canopy laser interception rate. Verified by 440 groups of height data measured manually in the field, the results show that the canopy laser interception compensation mechanism is of great help in improving the estimation accuracy of LiDAR. R2 and RMSE reach 0.90 and 6.18 cm, respectively. Compared with the estimation method before compensation, R2 is increased by 13.92%, and RMSE is reduced by 49.31%. And when the canopy interception rate is greater than 99%, the compensation effect is more obvious, and the RMSE is reduced by 62.49%. This research result can significantly improve the height estimation accuracy of UAV-borne for high planting density cotton areas, which is helpful to improve the efficiency of cotton quality breeding and match genomics data.

Scattering direction sampling methods for polarized Monte Carlo simulation of oceanic lidar.

Monte Carlo techniques have been widely applied in polarized light simulation. Based on different preconditions, there are two main types of sampling strategies for scattering direction: one is the scalar sampling method; the others are polarized sampling approaches, including the one- and two-point rejection methods. The polarized simulation of oceanic lidar involves a variety of mediums, and an efficient scattering sampling method is the basis for the coupling simulation of the atmosphere and ocean. To determine the optimal scattering sampling method for oceanic lidar simulation, we developed a polarized Monte Carlo model and simulated Mie scattering, Rayleigh scattering, and Petzold average-particle scattering experiments. This simulation model has been validated by comparison with Ramella-Roman's program [Opt. Express13, 4420 (2005)OPEXFF1094-408710.1364/OPEX.13.004420], with differences in reflectance and transmittance Stokes less than 1% in Mie scattering. The simulation results show these scattering sampling methods differ in runtime, scattering angle distributions, and reflectance and transmittance Stokes. Considering the current simulation accuracy of oceanic lidar, the differences in reflectance and transmittance Stokes are acceptable; thus, the runtime becomes the main evaluation factor. The one-point rejection method and scalar sampling method are preferable for the oceanic lidar polarized simulation. Under complex atmosphere-ocean coupling systems, scalar sampling methods may be a better choice since the calculation process of the sampling is independent of the incident Stokes vector.

Improving the Accuracy of TOF LiDAR Based on Balanced Detection Method.

The ranging accuracy of pulsed time-of-flight (TOF) lidar is affected by walk error and jitter error. To solve the issue, the balanced detection method (BDM) based on fiber delay optic lines (FDOL) is proposed. The experiments are carried out to prove the performance improvement of BDM over the conventional single photodiode method (SPM). The experimental results show that BDM can suppress common mode noise and simultaneously shift the signal to high frequency, which reduces the jitter error by approximately 52.4% and maintains the walk error at less than 300 ps with a non-distorted waveform. The BDM can be further applied to silicon photomultipliers.

Pulse Lidar imaging algorithm based on adaptive triangle window-width centroid discrimination

The pulse light detecting and ranging (Lidar) can obtain rich environmental information of the target, and the centroid algorithm can obtain the distance and intensity information of the target from the echo pulse. However, there are problems of sensitivity to noise and fixed window width. Therefore, this paper proposes an adaptive triangle window-width centroid algorithm (ATWC) to improve the ranging accuracy and robustness of Lidar. This algorithm combines the characteristics of the steep front and slow back of the pulse echo waveform. The centroid calculation point is selected by an isosceles triangle envelope in the time domain and a non-isosceles triangle envelope in the intensity domain with a longer front and a shorter back. Further combined with intensity weighting, the distance and intensity values of ATWC are obtained. On the one hand, the simulation verifies the influence of window width selection on the accuracy of the centroid algorithm. On the other hand, real environment comparison experiments are conducted with traditional waveform centroid (TWC), peak ranging (PR), and adaptive threshold methods (ATM) under a pulse Lidar system constructed by our method, and our method achieves significant improvements. At different distances, when the peak signal-to-noise ratio (PSNR) is above 20 dB, the ranging error of ATWC is below 0.3 ns, which is much better than the compared algorithms. When the target is at a distance of 25 m, which is a weak echo signal environment with a PSNR of 15.6 dB, our algorithm can still achieve a ranging error of 0.56 ns, and the detection success rate can still reach 88.8%. In addition, our method can obtain a clearer intensity image, which further demonstrates the research potential and practical utility of the proposed method.

Evaluating the accuracy and quality of an iPad Pro's built-in lidar for 3D indoor mapping

This study aimed to assess the accuracy of the lidar sensor on an iPad Pro and its potential for creating 3D BIM. Data was collected through different scanning methods in various environmental conditions. The results showed that the iPad Pro's lidar sensor produced accurate point clouds in static acquisition experiments with point distances of less than 1 mm from their best-fitting plane. However, in dynamic acquisition experiments, the distance increased to an average of 1 cm due to changes in the iPad Pro's pose. The study also found that the accuracy of the registered point clouds was better when scanning smaller areas with limited pose changes. Based on the findings, the study recommends maintaining a distance of 1–1.5 m between the iPad Pro and the target, as the target's color has minimal impact on scanning accuracy. Avoiding large posture changes while scanning can improve the accuracy of the point clouds in scan-to-BIM projects.

Uncontrolled Two-Step Iterative Calibration Algorithm for Lidar–IMU System

Calibration of sensors is critical for the precise functioning of lidar-IMU systems. However, the accuracy of the system can be compromised if motion distortion is not considered. This study proposes a novel uncontrolled two-step iterative calibration algorithm that eliminates motion distortion and improves the accuracy of lidar-IMU systems. Initially, the algorithm corrects the distortion of rotational motion by matching the original inter-frame point cloud. Then, the point cloud is further matched with IMU after the prediction of attitude. The algorithm performs iterative motion distortion correction and rotation matrix calculation to obtain high-precision calibration results. In comparison with existing algorithms, the proposed algorithm boasts high accuracy, robustness, and efficiency. This high-precision calibration result can benefit a wide range of acquisition platforms, including handheld, unmanned ground vehicle (UGV), and backpack lidar-IMU systems.

Investigatin the accuracy of iPhone LiDAR in preparing point clouds of tree trunks (Case study: Middle Zagros - Oak forests of Lorestan province)

investigatin the accuracy of iPhone LiDAR in preparing point clouds of tree trunks (Case study: Middle Zagros - Oak forests of Lorestan province)

Application of CEEMDAN combined wavelet threshold denoising algorithm to suppressing scattering cluster in underwater lidar

<sec>The echo of underwater lidar often contains a significant quantity of scattering clutters. In order to effectively suppress this scattering clutter and improve the ranging accuracy of underwater lidar, a novel denoising method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet threshold denoising is proposed.</sec><sec>The CEEMDAN-wavelet threshold denoising algorithm uses the correlation coefficient to select intrinsic mode function (IMF) components obtained from the CEEMDAN decomposition. The IMFs, which are more closely related to the original signal, are selected. Then, the wavelet thresholding denoising algorithm is applied to each of the selected IMFs to perform additional denoising. For each IMF component, specific threshold values are calculated based on their frequency and amplitude characteristics. Subsequently, the wavelet coefficients of the IMF components are processed by using these threshold values. Finally, the denoised IMF components are combined and reconstructed to obtain the final denoised signal. Applying the wavelet threshold denoising algorithm to IMF components can effectively remove noise components that cannot be removed by traditional CEEMDAN partial reconstruction methods. By using the threshold value calculated based on the characteristics of each IMF component, the wavelet thresholding denoising process is improved in comparison with directly using a single threshold value. This approach enhances the algorithm’s adaptability and enables more effective removal of noise from the signal.</sec><sec>We apply the proposed method to underwater ranging experiments. A 532 nm intensity-modulated continuous wave laser is used as a light source. Ranging is performed for a target in water with varying attenuation coefficients. A white polyvinyl chloride (PVC) reflector is used as a target. When the correlation extreme value is directly used to determine the delay at a distance of 3.75 attenuation length, it results in a ranging error of 19.2 cm. However, after applying the proposed method, the ranging error is reduced to 6.2 cm, thus effectively improving the ranging accuracy. These results demonstrate that the method has a significant denoising effect in underwater lidar system.</sec>

3D Mapping and Positioning Technology of Lunar Environment Based on LiDAR

-Using intelligent guidance system for lunar sample collection, Simultaneous lunar environment 3D mapping and localization technology (SLAM) is a key problem to be solved urgently. The lunar illumination changes dramatically and the terrain is complex. And limited computing power of the intelligent guidance system on the moon will affect the positioning accuracy of the algorithm. In view of the low positioning accuracy of LiDAR in the current lunar environment. This paper use an algorithm with higher positioning accuracy, which integrates the front-end feature extraction algorithm, the back-end optimization algorithm based on factor graph and the Scan Context loop closure detection algorithm. The simulation platform of lunar environment is built by Airsim to test the algorithm, and the robustness and accuracy of the algorithm are verified.

A systematic comparison of the accuracy of monocular RGB tracking and LiDAR for neuronavigation.

With the advent of augmented reality (AR), the use of AR-guided systems in the field of medicine has gained traction. However, the wide-scale adaptation of these systems requires highly accurate and reliable tracking. In this work, the tracking accuracy of two technology platforms, LiDAR and Vuforia, are developed and rigorously tested for a catheter placement neurological procedure. Several experiments (900) are performed for each technology across various combinations of catheter lengths and insertion trajectories. This analysis shows that the LiDAR platform outperformed Vuforia; which is the state-of-the-art in monocular RGB tracking solutions. LiDAR had 75% less radial distance error and 26% less angle deviation error. Results provide key insights into the value and utility of LiDAR-based tracking in AR guidance systems.

A New Data Processing Method for High-Precision Mining Subsidence Measurement Using Airborne LiDAR

Coal resources are the principal energy in China, and the surface subsidence caused by coal mining has a serious impact on the safe production and life of human beings. The traditional observation method of rock movement is slow and laborious, while the accuracy of airborne LiDAR, InSAR and other methods is relatively low. In this paper, aiming at the problem of the low accuracy of deformation monitoring of airborne LiDAR, the data registration of LiDAR point cloud is analyzed by combining theoretical analysis with field experiment. An advanced distribution mode of control points is discussed, and a current method of multi-period point cloud registration using seven-parameter transformation is proposed to obtain a surface subsidence model for mining area with high precision. The results show that the RMSE of airborne LiDAR is decreased from 0.013 m to 0.008 m by using the new method for data registration, and the maximum error value is reduced from 0.022 m to 0.014 m, which effectively enhances the deformation monitoring capability of airborne LiDAR.

Applicability of wind products retrieved from wind lidar in the winter games

Abstract Based on the 10-min average wind speed and wind direction data observed by automatic stations at the starting point and midway of the snow track in the “14th National Winter Games”, the wind product accuracy of wind lidar from December 1, 2019 to January 31, 2020 was analyzed. The results show that the mean relative deviation (MRD), standard deviation of relative deviation (STDRD ) and correlation coefficient (R) of wind direction data are better than wind speed data. The MRD is largest when the average wind speed of the ground observation is about 3 m/s, reaching 55%. The wind speed retrieved by the lidar is higher than that observed by automatic station when the average wind speed is lower than 10 m/s. When the average wind speed is higher than 10 m/s, the wind speed retrieved by the lidar is lower than ground observations. The detection accuracy of the lidar wind direction at the automatic station No.4 is better than that at the automatic station No.3. For the detection of wind speed, the mean absolute error (MAE) is larger, the R was slightly lower, but the mean error (ME), the MRD and the STDRD are better at the automatic station No.4. As the SNR increases, ME of the average wind speed decreases, and the relative deviations (RD) of wind speed and wind direction gradually concentrate on zero.

LiDAR-Visual-Inertial Odometry Based on Optimized Visual Point-Line Features

This study presents a LiDAR-Visual-Inertial Odometry (LVIO) based on optimized visual point-line features, which can effectively compensate for the limitations of a single sensor in real-time localization and mapping. Firstly, an improved line feature extraction in scale space and constraint matching strategy, using the least square method, is proposed to provide a richer visual feature for the front-end of LVIO. Secondly, multi-frame LiDAR point clouds were projected into the visual frame for feature depth correlation. Thirdly, the initial estimation results of Visual-Inertial Odometry (VIO) were carried out to optimize the scanning matching accuracy of LiDAR. Finally, a factor graph based on Bayesian network is proposed to build the LVIO fusion system, in which GNSS factor and loop factor are introduced to constrain LVIO globally. The evaluations on indoor and outdoor datasets show that the proposed algorithm is superior to other state-of-the-art algorithms in real-time efficiency, positioning accuracy, and mapping effect. Specifically, the average RMSE of absolute trajectory in the indoor environment is 0.075 m and that in the outdoor environment is 3.77 m. These experimental results can prove that the proposed algorithm can effectively solve the problem of line feature mismatching and the accumulated error of local sensors in mobile carrier positioning.

Influence of sampling frequency and laser pulse width on ranging accuracy of full-waveform LiDAR

Influence of sampling frequency and laser pulse width on ranging accuracy of full-waveform LiDAR

IoT-based low-cost 3D mapping using 2D Lidar for different materials

Three-dimensional (3D) mapping is the technology of profiling objects in three dimensions to map them in real life. 3D mapping is an excellent tool for analysis, surveying, and other engineering domains. 3D light detection and ranging (Lidar) technology usually does 3D mapping, but the cost of 3D Lidar is very high, which constrains its applicability in the Industry. The need for a low-cost 3D mapping has increased widely. A cost-effective 3D mapping based on the Internet of Things (IoT) using 2D Lidar is proposed. 2D Lidar is lower in cost than 3D Lidar, so it completely replaces the 3D Lidar for applications that need moderate or low accuracy. This research sought to confirm the applicability of this low-cost IoT-based advanced sensing technology, i.e., Lidar, in the engineering domain. A two-dimensional Lidar mapping system prototype is constructed, and the same is used to generate three-dimensional (3D) point cloud models. Lidar measurements are greatly dependent on the materials. Every material has different absorbance and reflectivity, which alter the Lidar measurements. In this research, we have shown how different material affects Lidar accuracy.

Seamless Vehicle Positioning by Lidar-GNSS Integration: Standalone and Multi-Epoch Scenarios

Accurate and seamless vehicle positioning is fundamental for autonomous driving tasks in urban environments, requiring the provision of high-end measuring devices. Light Detection and Ranging (lidar) sensors, together with Global Navigation Satellite Systems (GNSS) receivers, are therefore commonly found onboard modern vehicles. In this paper, we propose an integration of lidar and GNSS code measurements at the observation level via a mixed measurement model. An Extended Kalman-Filter (EKF) is implemented to capture the dynamic of the vehicle movement, and thus, to incorporate the vehicle velocity parameters into the measurement model. The lidar positioning component is realized using point cloud registration through a deep neural network, which is aided by a high definition (HD) map comprising accurately georeferenced scans of the road environments. Experiments conducted in a densely built-up environment show that, by exploiting the abundant measurements of GNSS and high accuracy of lidar, the proposed vehicle positioning approach can maintain centimeter-to meter-level accuracy for the entirety of the driving duration in urban canyons.