Among the existing LiDAR technologies, the LiDAR with frequency modulation continuous wave (FMCW) format has the advantages of high resolution, high measurement accuracy, light weight, low power consumption compared with the conventional time-of-flight LiDAR. Benefitting from adopting continuous lightwave for measure-ment, the FMCW LiDAR also has unique performances such as high sensitivity, rich information, and easy processing and demodulation. It is highly competitive for high-resolution, high-accuracy detection needs, and has the potential for very good integration, miniaturization, and low energy consumption. This paper introduces the basic principles of different LiDAR systems, especially focusing on the important parameters of FMCW LiDAR, and classi-fies the research work of FMCW LiDAR in the past ten years into different types based on the light source scheme, and discusses the features of various schemes.

LiDAR is a kind of active detection technology, which can acquire the three-dimensional spatial informa-tion of the target accurately and quickly. Due to its unique technical advantages in object recognition, classification, high-precision 3D imaging and measurement, the application scope and development prospect of LiDAR are quite broad. In this article, the principles of various LiDAR detection and 3D imaging systems are introduced, and the for-eign and domestic development status of single point scanning, linear array sweeping and planar array 3D imaging LiDAR systems are summarized and sorted out. Meanwhile, their technical characteristics, advantages and disad-vantages in different platforms and application fields such as spaceborne, airborne and vehicular platforms are compared and analyzed. Recently, 3D imaging LiDAR is gradually developed from single point scanning to small array scanning, line array sweep and array flash imaging. At the same time, the single photon detection technology is becoming mature and the detection sensitivity is getting higher and higher. With the development of modern de-tection technology more and more inclined to the fusion detection of various sensors, the development of 3D imaging is also inclined to the combination of active and passive imaging to obtain more abundant target information.

With the preparation of intelligent driving into industrialization and commercialization, LiDAR has become an indispensable environmental sensor with its excellent performance and has developed rapidly in hardware technology and related application algorithms. This paper introduces the key technologies of LiDAR hardware by using LiDAR scanning method and related technology as the entry point, discussing the principle, characteristics and current status of mechanical, hybrid and all-solid-state automotive LiDAR. Three kinds of task-oriented vehicle Li-DAR application algorithms, point cloud segmentation, target tracking and recognition, simultaneous location and mapping, are summarized. The analysis shows that the vehicle LiDAR will further become solid-state, intelligent and networked in order to reduce costs, improve performance and meet intelligent driving requirements; the pursuit of application algorithm research is real-time, efficient and reliable.

Light detection and ranging (LiDAR) system can be used to capture the distances and speeds of the tar-gets with high resolution and high accuracy, and can also form imaging. It is important for the applications such as mapping, and navigation, et al. This paper introduces the LiDAR solution based on micro-electromechanical system (MEMS) is a transitional scheme from mechanical one to solid-state. Meanwhile, in terms of the requirement of sol-id-state, the principles of Flash and optical phased array LiDAR are introduced in this paper. At the same time, the miniaturization trend of LiDAR is presented with optical phased array based on liquid crystal (LC) and integrated optical waveguides. At last, the performances and open issues of the solutions for LiDAR are concluded and the development trends of LiDAR are summarized with outlook.

In the research of unmanned vehicle, the state estimation of target detected by sensors is one of the key issues in environmental sensing technology. In this paper, an algorithm based on unscented Kalman filter is pro-posed to predict and update the position of the target based on the obtained radar data, which is used to estimate the target position of the unmanned vehicle dual radar system. The vehicle radar system in this paper is composed of four lines laser and millimeter wave radar. The calibrated vehicle coordinate system is a two-dimensional coordinate system parallel to the ground. On the basis of the system and coordinate system, the real radar data are collected and simulated in the experimental site. Experiments show that compared with single sensor, the measurement error of radar combination model is effectively reduced, and the accuracy of fusion data is improved. Compared with the most commonly used extended Kalman filtering algorithm, the mean square error of the moving direction of vehicle descends from 6.15 per thousand to 4.83 per thousand. The mean square error value of the average position de-creases from 4.24 per thousand to 2.99 per thousand in the direction parallel to the front axle, which indicates that the estimation of the target position of this algorithm is more accurate and closer to the real value. In addition, in the same operating environment, the average time of processing 500 groups of radar data is reduced from 5.9 ms to 2.1 ms, proving that the algorithm has a higher algorithm efficiency.

Aiming at the problem of accurately segmenting the ground in real-time from 3D LiDAR point cloud, a ground segmentation algorithm based on the features of scanning line segments is proposed. The algorithm first performs de-noising and pose correction on the 3D point cloud, then divides the scanning line according to the Euclidean distance and absolute height difference between adjacent points. Next, the characteristics of the adjacent line segments such as spacing, slope, and absolute height difference are analyzed. The maximum likelihood estimation is used to solve the feature threshold function, which improves the adaptability of threshold. Finally, comprehensively considering the undulating and inclined complex terrain, the scanning line segments are marked as segments of flat ground, segments of slope and segments of obstacle by formulating the new horizontal and vertical classification strategies. This algorithm has been successfully applied to the unmanned ground platform. The usage and comparative test show that the algorithm can detect the ground stably and efficiently in both urban and field scenarios.

As an important part of intelligent vehicle, environmental perception system mainly refers to the detection of the surrounding environment of the vehicle by the sensors attached on the vehicle. In order to ensure the accuracy and stability of the intelligent vehicle environmental perception system, it is necessary to use intelligent vehicle sensors to detect and track objects in the passable area. In this paper, an object detection and tracking algorithm based on the LiDAR and camera information fusion is proposed. The algorithm uses the point cloud data clustering method of LiDAR to detect the objects in the passable area and project them onto the image to determine the tracking objects. After the objects are determined, the algorithm uses color information to track objects in the image sequence. Since the object tracking algorithm based on image is easily affected by light, shadow and background interference, the algorithm uses LiDAR point cloud to modify the tracking results. This paper uses KITTI data set to verify and test this algorithm and experiments show that the target area detection overlap of the proposed target detection and tracking algorithm is 83.10% on average and the tracking success rate is 80.57%. Compared with particle filtering algorithm, the average region overlap increased by 29.47% and the tracking success rate increased by 19.96%.

In the process of obstacle detection based on LiDAR, the traditional DBSCAN clustering algorithm can’t achieve good clustering for both short-range and long-distance targets because of the uneven distribution of data density, resulting in missed detection or false detection. To solve the problem, this paper proposed an optimized DBSCAN algorithm which improves the adaptability under different distance by optimize the selection method of neighborhood radius. According to the distribution of the lines scanned by LiDAR, the distance between two adjacent scan lines is determined and an improved neighborhood radius list is established. Then the neighborhood radius will be searched in the list based on the coordinated values of each scan point. Finally, linear interpolation method is used to obtain the corresponding neighborhood radius. The experimental results based on Ford dataset prove that compared with the traditional DBSCAN algorithm, the proposed algorithm can effectively improve the accuracy of obstacle detection and adapt to the target clustering operation under different distances. The positive detection rate of obstacle detection is increased by 17.52%.

To carry out the measurement of vehicle body position and dimension of loading robot before loading, an intelligent vehicle body measurement system based on two-dimensional LiDAR was provided, and the calibration method of this system was studied as a key point. The two-dimension LiDAR was driven by rotating the platform, and the three-dimensional information of car body measured was obtained by using the single two-dimensional laser radar. In allusion to the complexity of calibration method of LiDAR measurement system and the difficulty in making calibration pieces, a system parameter calibration method was proposed based on 321 coordinate system building method, and mathematical models of calibration was established, with the principle and procedure of calibration method in detail. Measurement system was set up in a laboratory to carry out calibration experiment and mea-surement experiment on simulation vehicle body, and the measurement experiment for real vehicle body was con-ducted outside. The experiment result shows that the maximum measurement error of vehicle body size and length of this measurement system was 26.4 mm; maximum angle measurement error was 0.18 degree, which fully meets the precision requirements of loading.

The detection of CO2 based on coherent different absorption LiDAR (CDIAL) requires high signal-to-noise ratio (SNR). To improve the SNR and reduce the inversion error of CO2, a coherent differential absorption LiDAR based on Golay coding is proposed and the corresponding decoding method is also studied. The coding gain of SNR in traditional atmospheric backscattering signal detection is also analyzed when the pulse code technology is used. The variations of coding gain with the power of local oscillator (LO), the code length and the splitting ratio of 3 dB coupler are discussed. The higher the local oscillator power is and the more the beam splitting ratio deviates from 50%, the lower the coding gains. In addition, there are optimal code lengths in practical systems. The influence of thermal noise on the detection system decreases when the LO power grows, and there is optimal LO power which is only related to the system noise characteristics. The optimal LO power decreases with respect to single pulse de-tection after pulse coding, but the SNR is still higher than the traditional single pulse detection. When the splitting ratio of the 3 dB coupler is 0.495, the optimal LO power in coded system is 0.93 mW. The effective detection ranges of CO2 increase when the pulses are coded, and in the pulse accumulations of 104～1010, the improvement ratios of effective detection range are higher than 15%.

In order to solve the flight safety issues threatened by wake vortex of leading aircraft, ensure air traffic safety, and improve the capacity of airdrome and airspace, an AlexNet convolutional neural network model algorithm is proposed to identify aircraft wake vortex. Combined with the detection principle of Doppler LiDAR and the classic model of Hallck-Burnham wake vortex velocity, the AlexNet neural network model was constructed to extract the image features of the wake vortex velocity images in the atmosphere and identify the aircraft wake vortex. The re-search shows that the model is able to accurately identify the aircraft wake vortex in the target airspace. After the network model converges, the accuracy rate reaches to 91.30%, which can effectively realize the identification work. Meanwhile, this study also demonstrates the low probability of false alarm of the AlexNet neural network in detecting wake vortex, which meets the requirement of early warning and monitoring of the aircraft wake vortex.

The hardware fault of the LiDAR will make the quality of the echo data worse. However, there is still a lack of effective identification methods for the error data caused by the hardware failure. Analysis of echo characteristics of atmospheric particulate matter monitoring when LiDAR has hardware failure, according to the echo signal infor-mation of the echo shape and intensity of the LiDAR, the fuzzy logic algorithm is used to identify the fault data. The hardware fault data of the atmospheric particulate LiDAR is identified and tested. At the same time, in order to re-duce the false positive rate of data without hardware failures, the mean values of extinction coefficient and sig-nal-to-noise ratio (SNR) at the height of 300 meters to 500 meters were compared between the data of hardware failures and the data was misjudged, reduced the false positive rate by setting the signal to noise ratio threshold. The experimental results show that this method is used to identify the hardware fault data of the LiDAR monitoring of the external field, the recognition rate is 94.6%, and the false positive rate is only 1.5%. This method has a good recog-nition effect on hardware fault data.

This paper presents a high bandwidth and lownoise fully differential main amplifier (FDMA) for pulsed time-of-flight (TOF) imaging laser detection and ranging application (LADAR), which serves to amplify the small pulse echo signal. The cascaded architecture and active inductor technology are used to enlarge the bandwidth of the circuit and reduce the chip area. The cascaded gain stages, which adopted DC offset isolation circuit, are more robust to the alteration of process. A large bandwidth amplifier (LBA) and an output buffer (OB) structure have been designed to enhance the drive capabilities. Besides, in order to adapt the demand of the LADAR system, the am-plifier receiver’s bandwidth has been limited by using an inter-stage bandpass filter. Implemented in CSMC CMOS technology, the FDMA chip realizes the -3 dB bandwidth of 730.6 MHz, and an open loop gain of 23.5 dB with the bandpass filter worked. The input-referred noise voltage is 2.7 nV/sqrt(Hz), which effectively reduces the system noise. This chip that occupies 0.25 mm×0.25 mm in area consumes a power dissipation of 102.3 mW from the 3.3 V power supply. As a part of the integrated chip of the laser radar system, it can better meet the requirements of sys-tem.