The point measurement laser absorption spectroscopy (PMLAS) based on saturated absorption theory could surpass the defect of 'line-of-sight' measurement in traditional tunable diode laser absorption spectroscopy (TDLAS) and achieve the 'point' measurement with millimeter spatial resolution. It is realized by crossing with two frequency synchronized laser beams: one named probe beam as in traditional TDLAS and the other named saturated beam with higher power. In this paper, the theory of PMLAS was firstly analyzed by the theoretical deduction of saturated absorption coefficients with arbitrary cross angles and the numerical calculations of point absorbance under different saturation parameters. Next, a weak signal detection method based on high-frequency sinusoidal modulation of the saturated beam intensity was proposed, in which the first-order harmonic signal was theoretically deduced and verified by numerical demonstration. Furthermore, it is found that the FWHMs (full width at half maximum) of different order harmonics are all the same and equal to the width of the absorption signal without modulation, which implied that the superposition of multi-harmonics could enhance the signal-to-noise ratio (SNR) in measuring the spectrum line-width.

To solve the problem of low stability of fusion validity measurement in existing fusion of infrared intensity and polarization images, the stability of various fusion algorithms for the distribution of fusion validity of different features was analyzed and compared by using three common fusion validity function measurement methods based on measurement distance. By calculating the frequency of the optimal fusion algorithm in difference feature amplitude interval of ten groups of images, the optimal fusion algorithms for each difference feature were obtained, and cosine similarity was obtained as a measure of fusion efficiency with high stability and more consistent with subjective observation results of the humans. The experimental results show that the cosine similarity has high stability and good matching with human vision analysis in the fusion effectiveness measurement of various fusion algorithms.

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 information 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 reduce the false positive rate of data without hardware failures, the mean values of extinction coefficient and signal-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 recognition effect on hardware fault data.

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 measurement experiment on simulation vehicle body, and the measurement experiment for real vehicle body was conducted 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.

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.

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 detection 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%.

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%.

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 LiDAR 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.

As a new non-invasive and high-resolution scanning method, optical coherence tomography (OCT) has been widely used in clinical practice, but OCT images have serious speckle noise, which greatly affects the diagnosis of diseases. Two original dictionary noise reduction algorithms have been improved for multiplicative speckle noise in OCT. The algorithm first performs logarithmic transformation on OCT images, uses orthogonal matching pursuit algorithm for sparse coding, and K singular value decomposition learning algorithm to update adaptive dictionary. Finally, it returns to the space domain through weighted average and exponential transformation. The experimental results show that the improved two dictionary algorithms can effectively reduce the speckle noise in OCT images and obtain good visual effects. The noise reduction effect is evaluated by four factors: mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and edge-preserving index (EPI). Compared with the two original dictionary noise reduction algorithms and the traditional filtering algorithms, the noise reduction effect of the two improved dictionary algorithms is better than that of other algorithms, and the improved adaptive dictionary algorithm performs better.