Search Results (32)

Simultaneous Localization and Mapping (SLAM) is crucial for the safe navigation of autonomous systems. Its accuracy is not based solely on the robustness of the algorithm employed or the metrological performances of the sensor, but rather on a combination of both factors. In this work, we present a comprehensive comparison framework for evaluating LiDAR-SLAM systems, focusing on key performance indicators including absolute trajectory error, uncertainty, number of tracked features, and computational time. Our case study compares two LiDAR-inertial SLAM configurations: one based on a motorized optomechanical scanner (the Ouster OS1) with a 360° field of view and the other based on MEMS scanners (the Livox Horizon) with a limited field of view and a non-repetitive scanning pattern. The sensors were mounted on a UGV for the experiments, where data were collected by driving the UGV along a predefined path at different speeds and angles. Despite substantial differences in field of view, detection range, and noise, both systems demonstrated comparable trajectory estimation performance, with average absolute trajectory errors of 0.25 m for the Livox-based system and 0.24 m for the Ouster-based system. These findings underscore the importance of sensor–algorithm co-design and demonstrate that even cost-effective, lower-field-of-view solutions can deliver competitive SLAM performance in real-world conditions. Full article

Lidar has been extensively used in various applications, such as autonomous driving, robot navigation, and drone obstacle avoidance, due to its advantages of a high resolution, high-ranging accuracy, and strong anti-interference ability. The micro-electro-mechanical systems (MEMS) lidar technology approach has gained popularity due to its miniaturization and semi-solid state. However, the small scanning angle of the MEMS scanning micromirror and the associated radar system cause issues, such as a limited scanning range and low spatial resolution, which hinder the wider use of MEMS lidar. To address the problems caused by the small scanning angle of the MEMS micromirror and the limitations of the current optical system, this study suggests a new MEMS lidar transmitting optical system that offers a wide scanning angle and high spatial resolution. It is based on an array reflector group and a Fresnel lens, which enables the large-angle scanning of the target area while maintaining high spatial resolution. The scanning range is 120° × 60°, the spatial resolution is 0.05° × 0.25°, and the beam-filling ratio reaches 90.63%. Full article

This article aims to utilize a microelectromechanical system (MEMS) to modulate coupling behavior of silicon nitride (Si₃N₄) waveguides to perform an optical switch based on a directional coupling (DC) mechanism. There are two states of the switch. First state, a Si₃N₄ wire is initially positioned up suspended in the air. In the second state, this wire will be moved down to be placed between two arms of the DC waveguides, changing the coupling behavior to achieve bar and cross states of the optical switch function. In the future, the MEMS will be used to move this wire down. In this work, we present simulations of the two static states to optimize the DC structure parameters. Based on the simulated results, the device size is 8.8 μm × 55 μm. The insertion loss is calculated to be approximately 0.24 dB and 0.33 dB, the extinction ratio is approximately 24.70 dB and 25.46 dB, and the crosstalk is approximately −24.60 dB and −25.56 dB, respectively. In the C band of optical communication, the insertion loss ranges from 0.18 dB to 0.47 dB. As such, this device will exhibit excellent optical switch performance and provide advantages in many integrated optics-related optical systems applications. Furthermore, it can be used in optical communications, data centers, LiDAR, and so on, enhancing important reference value for such applications. Full article

A MEMS scanning mirror is a beam scanning device based on MEMS technology, which plays an important role in the fields of Lidar, medical imaging, laser projection display, and so on. The accurate measurement of the scanning mirror index can verify its performance and application scenarios. This paper designed and built a scanning mirror benchmark platform based on a two-dimensional position-sensitive detector (PSD), which can accurately measure the deflection angle, resonance frequency, and angular resolution of the scanning mirror, and described the specific test steps of the scanning mirror parameters, which can meet the two-dimensional measurement. Secondly, this paper analyzed and calculated the angular test uncertainty of the designed test system. After considering the actual optical alignment error and PSD measurement error, when the distance between the PSD and MEMS scanning mirror is 100 mm, the range of mechanical deflection angle that can be measured is (−6.34°, +6.34°). When the mechanical deflection angle of the scanning mirror is 0.01°, the accuracy measured by the test system is 0.00097°, and when the mechanical deflection of the scanning mirror is 6.34°, the accuracy measured by the test system is 0.011°. The test platform has high accuracy and can measure the parameters of the scanning mirror accurately. Full article

MEMS-based LiDAR has showcased extensive application potential in the autonomous driving sector, attributed to its cost-effectiveness, compactness, and seamless integration capabilities. However, MEMS LiDAR suffers from a short detection range, due to the small receiving aperture of the MEMS mirror. Our early study attempted to increase the detection range of MEMS LiDAR with a semi-coaxial design. In this paper, we further investigate the synchronization method for large-aperture electromagnetic MEMS mirrors, in which a synchronous motion transfer model of electromagnetic MEMS mirrors is constructed. The results of the simulations and experiments demonstrate that two electromagnetic MEMS mirrors are synchronous with an aperture of 60 ${\mathsf{\pi }\mathrm{mm}}^2$, FoV of 60°, and scanning frequency of 220 Hz. The entire synchronization process of the electromagnetic MEMS mirrors is completed within 10 s, which verifies the feasibility of synchronizing large-aperture electromagnetic MEMS mirrors to increase the detection range of MEMS LiDAR. Full article

Global Navigation Satellite Systems (GNSSs) frequently encounter challenges in providing reliable navigation and positioning within urban canyons due to signal obstruction. Micro-Electro-Mechanical System (MEMS) Inertial Measurement Units (IMUs) offers an alternative for autonomous navigation, but they are susceptible to accumulating errors. To mitigate these influences, a LiDAR-based Simultaneous Localization and Mapping (SLAM) system is often employed. However, these systems face challenges in drift and error accumulation over time. This paper presents a novel approach to loop closure detection within LiDAR-based SLAM, focusing on the identification of previously visited locations to correct time-accumulated errors. Specifically, the proposed method leverages the vehicular drivable area and IMU trajectory to identify significant environmental changes in keyframe selection. This approach differs from conventional methods that only rely on distance or time intervals. Furthermore, the proposed method extends the SCAN CONTEXT algorithm. This technique incorporates the overall distribution of point clouds within a region rather than solely relying on maximum height to establish more robust loop closure constraints. Finally, the effectiveness of the proposed method is validated through experiments conducted on the KITTI dataset with an enhanced accuracy of 6%, and the local scenarios exhibit a remarkable improvement in accuracy of 17%, demonstrating improved robustness in loop closure detection for LiDAR-based SLAM. Full article

Short-range MEMS-based (Micro Electronical Mechanical System) LiDAR provides precise point cloud datasets for rock fragment surfaces. However, there is more vibrational noise in MEMS-based LiDAR signals, which cannot guarantee that the reconstructed point cloud data are not distorted with a high compression ratio. Many studies have illustrated that wavelet-based clustered compressive sensing can improve reconstruction precision. The k-means clustering algorithm can be conveniently employed to obtain clusters; however, estimating a meaningful k value (i.e., the number of clusters) is challenging. An excessive quantity of clusters is not necessary for dense point clouds, as this leads to elevated consumption of memory and CPU resources. For sparser point clouds, fewer clusters lead to more distortions, while excessive clusters lead to more voids in reconstructed point clouds. This study proposes a local clustering method to determine a number of clusters closer to the actual number based on GMM (Gaussian Mixture Model) observation distances and density peaks. Experimental results illustrate that the estimated number of clusters is closer to the actual number in four datasets from the KEEL public repository. In point cloud compression and recovery experiments, our proposed approach compresses and recovers the Bunny and Armadillo datasets in the Stanford 3D repository; the experimental results illustrate that our proposed approach improves reconstructed point clouds’ geometry and curvature similarity. Furthermore, the geometric similarity increases to 0.9 above in our complete rock fragment surface datasets after selecting a better wavelet basis for each dimension of MEMS-based LiDAR signals. In both experiments, the sparsity of signals was 0.8 and the sampling ratio was 0.4. Finally, a rock outcrop point cloud data experiment is utilized to verify that the proposed approach is applicable for large-scale research objects. All of our experiments illustrate that the proposed adaptive clustered compressive sensing approach can better reconstruct MEMS-based LiDAR point clouds with a lower sampling ratio. Full article

Sensors based on MEMS technology, in particular Inertial Measurement Units (IMUs), when installed on vehicles, provide a real-time full estimation of vehicles’ state vector (e.g., position, velocity, yaw angle, angular rate, acceleration), which is required for the planning and control of cars’ trajectories, as well as managing the in-car local navigation and positioning tasks. Moreover, data provided by the IMUs, integrated with the data of multiple inputs from other sensing systems (such as Lidar, cameras, and GPS) within the vehicle, and with the surrounding information exchanged in real time (vehicle to vehicle, vehicle to infrastructure, or vehicle to other entities), can be exploited to actualize the full implementation of “smart mobility” on a large scale. On the other hand, “smart mobility” (which is expected to improve road safety, reduce traffic congestion and environmental burden, and enhance the sustainability of mobility as a whole), to be safe and functional on a large scale, should be supported by highly accurate and trustworthy technologies based on precise and reliable sensors and systems. It is known that the accuracy and precision of data supplied by appropriately in-lab-calibrated IMUs (with respect to the primary or secondary standard in order to provide traceability to the International System of Units) allow guaranteeing high quality, reliable information managed by processing systems, since they are reproducible, repeatable, and traceable. In this work, the effective responsiveness and the related precision of digital IMUs, under sinusoidal linear and curvilinear motion conditions at 5 Hz, 10 Hz, and 20 Hz, are investigated on the basis of metrological approaches in laboratory standard conditions only. As a first step, in-lab calibrations allow one to reduce the variables of uncontrolled boundary conditions (e.g., occurring in vehicles in on-site tests) in order to identify the IMUs’ sensitivity in a stable and reproducible environment. For this purpose, a new calibration system, based on an oscillating rotating table was developed to reproduce the dynamic conditions of use in the field, and the results are compared with calibration data obtained on linear calibration benches. Full article

This paper presents the optical system design for the MEMS mirror-based SPAD LiDAR system. The transmitter of the proposed LiDAR system consists of related optics for incident beam expansion and a piezoelectric MEMS mirror for a wide-scanning field of view. For the receiver unit, an SPAD array is utilized to collect the laser beam reflected from the target objects at a smart factory. The optical system of the proposed LiDAR system is presented, designed, and analyzed in various ways. Full article

A new scheme presents MEMS-based LiDAR with synchronized dual-laser beams for detection range enhancement and precise point-cloud data without using higher laser power. The novel MEMS-based LiDAR module uses the principal laser light to build point-cloud data. In addition, an auxiliary laser light amplifies the single-noise ratio to enhance the detection range. This LiDAR module exhibits the field of view (FOV), angular resolution, and maximum detection distance of 45° (H) × 25° (V), 0.11° (H) × 0.11° (V), and 124 m, respectively. The maximum detection distance is enhanced by 16% from 107 m to 124 m with a laser power of 1 W and an additional auxiliary laser power of 0.355 W. Furthermore, the simulation results show that the maximum detection distance can be up to 300 m with laser power of 8 W and only 6 W if the auxiliary laser light of 2.84 W is used, which is 35.5% of the laser power. This result indicates that the synchronized dual-laser beams can achieve long detection distance and reduce laser power 30%, hence saving on the overall laser system costs. Therefore, the proposed LiDAR module can be applied for a long detection range in autonomous vehicles without requiring higher laser power if it utilizes an auxiliary laser light. Full article

Most automated vehicles (AVs) are equipped with abundant sensors, which enable AVs to improve ride comfort by sensing road elevation, such as speed bumps. This paper proposes a method for estimating the road impulse features ahead of vehicles in urban environments with microelectromechanical system (MEMS) light detection and ranging (LiDAR). The proposed method deploys a real-time estimation of the vehicle pose to solve the problem of sparse sampling of the LiDAR. Considering the LiDAR error model, the proposed method builds the grid height measurement model by maximum likelihood estimation. Moreover, it incorporates height measurements with the LiDAR error model by the Kalman filter and introduces motion uncertainty to form an elevation weight method by confidence eclipse. In addition, a gate strategy based on the Mahalanobis distance is integrated to handle the sharp changes in elevation. The proposed method is tested in the urban environment. The results demonstrate the effectiveness of our method. Full article

The Micro-Electro-Mechanical System (MEMS) LiDAR point cloud in autonomous vehicles has a large deflection range, which results in slow registration speed and poor applicability. To maximize speed, an improved Normal Distribution Transform (NDT) method that integrates point cloud density features has been proposed. First, the point cloud is reduced using a modified voxel filter and a pass-through filter. Next, the Intrinsic Shape Signature (ISS) algorithm is utilized to analyze the point cloud features and extract key points; the Four-Point Congruent Set (4PCS) algorithm is then employed to calculate the initial pose under the constraints of the key point set to complete the coarse registration. Finally, the self-adaptive segmentation model is constructed by using a K-D tree to obtain the density features of key points, and the NDT algorithm is combined with this model to form an SSM-NDT algorithm, which is used for fine registration. Each algorithm was compared on the autonomous vehicle dataset PandaSet and actual collected datasets. The results show that the novel method increases the speed by at least 60% and takes into account good registration accuracy and strong anti-interference. Full article

This paper presents the fabrication and characterization of a biaxial MEMS (MicroElectroMechanical System) scanner based on PZT (Lead Zirconate Titanate) which incorporates a low-absorption dielectric multilayer coating, i.e., a Bragg reflector. These 2 mm square MEMS mirrors, developed on 8-inch silicon wafers using VLSI (Very Large Scale Integration) technology are intended for long-range (>100 m) LIDAR (LIght Detection And Ranging) applications using a 2 W (average power) pulsed laser at 1550 nm. For this laser power, the use of a standard metal reflector leads to damaging overheating. To solve this problem, we have developed and optimised a physical sputtering (PVD) Bragg reflector deposition process compatible with our sol-gel piezoelectric motor. Experimental absorption measurements, performed at 1550 nm and show up to 24 times lower incident power absorption than the best metallic reflective coating (Au). Furthermore, we validated that the characteristics of the PZT, as well as the performance of the Bragg mirrors in terms of optical scanning angles, were identical to those of the Au reflector. These results open up the possibility of increasing the laser power beyond 2W for LIDAR applications or other applications requiring high optical power. Finally, a packaged 2D scanner was integrated into a LIDAR system and three-dimensional point cloud images were obtained, demonstrating the scanning stability and operability of these 2D MEMS mirrors. Full article

Measurement performance evaluation of real and virtual automotive light detection and ranging (LiDAR) sensors is an active area of research. However, no commonly accepted automotive standards, metrics, or criteria exist to evaluate their measurement performance. ASTM International released the ASTM E3125-17 standard for the operational performance evaluation of 3D imaging systems commonly referred to as terrestrial laser scanners (TLS). This standard defines the specifications and static test procedures to evaluate the 3D imaging and point-to-point distance measurement performance of TLS. In this work, we have assessed the 3D imaging and point-to-point distance estimation performance of a commercial micro-electro-mechanical system (MEMS)-based automotive LiDAR sensor and its simulation model according to the test procedures defined in this standard. The static tests were performed in a laboratory environment. In addition, a subset of static tests was also performed at the proving ground in natural environmental conditions to determine the 3D imaging and point-to-point distance measurement performance of the real LiDAR sensor. In addition, real scenarios and environmental conditions were replicated in the virtual environment of a commercial software to verify the LiDAR model’s working performance. The evaluation results show that the LiDAR sensor and its simulation model under analysis pass all the tests specified in the ASTM E3125-17 standard. This standard helps to understand whether sensor measurement errors are due to internal or external influences. We have also shown that the 3D imaging and point-to-point distance estimation performance of LiDAR sensors significantly impacts the working performance of the object recognition algorithm. That is why this standard can be beneficial in validating automotive real and virtual LiDAR sensors, at least in the early stage of development. Furthermore, the simulation and real measurements show good agreement on the point cloud and object recognition levels. Full article

Phase light modulator (PLM) by MEMS mirror array operating in a piston-mode motion enables a high-speed diffractive beam steering in a random-access and flexible manner that makes a lidar system more intelligent and adaptive. Diffraction efficiency is determined by the range of the piston motion of the MEMS array; consequently, a larger range of the piston motion is required for beam steering in infrared, such as for lidar. We demonstrated how the range of the piston motion is optically enhanced by a factor of two with a light-recycling optics based on Talbot self-imaging. The proposed optical architecture extends the usable range of the wavelength so that a MEMS-PLM designed for visible wavelength is applicable for a high-efficiency beam steering at an infrared wavelength of 1550 nm with an improved diffraction efficiency of 30%. Full article