LiDAR echo intensity information can reflect the reflection characteristics of the target surface, and can be used as an important data source in the aspects of LiDAR point cloud image vision, classification and feature extraction. Geiger mode avalanche photodiode (Gm-APD) has the ability of single photon detection and high range sensitivity, and is widely used in the field of lidar. The number of statistics is often taken as the target intensity information obtained. In order to make the intensity image accurately reflect the reflection characteristics of the target surface, a kind of intensity information correction method of Gm-APD lidar is proposed. By eliminating the distortion caused by the detection model and target distance of the detector, the average reflectivity estimation error can be increased from 51.97% to 8.86%. Aiming at Gm-APD lidar, the determination method of parameters in parameter estimation method is systematically described in this paper. On this basis, the calibration of the laser emitter can improve the uniformity of the target, and the standard deviation is increased from 1.1818 to 0.0050. The proposed scheme can provide a reliable data source for target recognition, classification and feature extraction based on Gm-APD intensity image.
Aiming at the problem that the background noise mixed in the target echo will affect the calculation of the target polarization degree when the traditional polarization detection system obtains the target polarization degree, based on the polarization Gm-APD detection model, a set of target echo polarization correction method is proposed. The target is imaged in a xenon lamp environment, the influence of target attitude and polarization angle on detection is explored, and the polarization imaging results are analyzed. The results show that the polarization system has a significant effect on metal materials with low surface roughness. When circularly polarized light is incident, the echo trigger probability of the metal material reaches a peak at the polarization angle of 135°. The greater the incident angle, the greater the echo depolarization and the lower the trigger probability. By inverting the distribution of echo photons, the number of background noise photons in the echo and the number of target echo photons can be obtained respectively, and a more accurate correction of the polarization degree of the target echo can be obtained. For metal materials, when the target attitude angle is 30°, the target polarization before and after correction are 0.47 and 0.57 respectively, and the target echo polarization after correction is 7% higher than that without correction. This research work provides experimental support for the effective detection and target detection of GM-APD lidar in the daytime.
Bi-directional reflection distribution function (BRDF) is a common method to study the laser scattering characteristics of targets, and it is an important parameter for the theoretical demonstration of laser active detection, target recognition and classification. Scholars at home and abroad have proposed many mature BRDF models to describe the scattering characteristics of different targets. However, almost all of these models do not take into account the effect of incident wavelength on scattering characteristics. In addition, limited by the frequency modulation range of the laser, the existing BRDF measurement devices cannot obtain the BRDF data of the target at any wavelength, which restricts the application of the existing BRDF model. In view of this limitation, a method is proposed to calculate the unknown wavelength BRDF data using the BRDF measurement data of known wavelengths. Firstly, based on the Kirchhoff approximation theory, the spatial distribution of the scattered light field of the metal aluminum target at any wavelength was simulated and analyzed. Secondly, the error of the theoretical simulation model was analyzed through the experimental data. Finally, the BRDF data at any wavelength were calculated using the simulation data and the experimental data with known wavelengths. The final results showed that at the 1064nm wavelength, the RMSE value of the calculated data obtained by this method is 0.3553, which is 0.2233 smaller than the RMSE value of the simulation data.This method is effective in calculating the BRDF of metal aluminum targets at different wavelengths.
Edges are critical important for the visual appearance of images. The traditional denoising algorithms are difficult to preserve the edges of the image while removing the noise of ICCD sensing image. At the same time, it is difficult to eliminate the problems of image darkness and low resolution caused by uneven illumination. This paper proposes a multilevel filtering image denoising algorithm based on edge information fusion. The target edges detection of the image after non-local means (NL-means) filtering is carried out based on the eight-direction Sobel operator. In order to filter the false edge points and residual noise, an adaptive threshold is determined according to the mean and variance of the eight neighborhood pixels of the detected pixel. Meanwhile, homomorphic filtering is used to enhance the image contrast and uniformity. By comparing the pixel values of the edge image and the homomorphic filtered image, the final denoised image is obtained by fusing the two images. The results indicate that, compared with the traditional algorithms, the edge preserving ability of the proposed algorithm is improved by more than 20%, and the denoising ability is improved by 63.5% for building target. For specific targets (vehicle), the results demonstrate that the proposed algorithm have the maximum edge preserving index and contrast, and the minimum non-uniformity. This algorithm lays a foundation for target segmentation and recognition.
Gm-APD arrays lidar has the advantages of long imaging distance, small volume and low power consumption. Because of its unique high resolution three-dimensional range profile, it is expected to solve the problems of UAV safe flight, autonomous obstacle avoidance and so on. In this paper, according to the dynamic imaging requirements of UAV lidar, a joint image stabilization control algorithm of adaptive Kalman filter and PID is proposed to suppress the disturbance of UAV platform to lidar system and make the laser beam point to the target stably. The vibration test experiment of Gm- APD lidar system is made. Under the condition of horizontal amplitude 5mm and frequency 15Hz sine wave disturbance, the gyro drift is less than 1.7 °/ s, and the target drift is no more than 4 pixels. It is proved that Gm-APD lidar can be applied in the field of UAV safe flight.
In the process of underwater lidar wake detection, the multipath effect leads to pulse stretching of the echo signal, which is a distinguishing feature to distinguish the wake echo signal. In order to explore the factors that affect the pulse stretching of the echo signal, this paper convolves the instantaneous echo energy of the bubbles at different distance layers with the transmitted pulse, and establishes a distance layer summation model(DLSM) of underwater bubbles. This paper analyzes the effect of bubble density on the backscattering coefficient of the bubbles and the pulse width of the echo signal, and introduces the attenuation length of the water and the multipath effect. The experimental results show that when the attenuation length increases from 1.2 to 2.0, the half-peak width of the echoes of the bubbles increases by 1.3ns. However, when the attenuation length increases from 1.5 to 2.2, the echo pulse width of the strong backscatter target decreases by 0.5ns. The thickness of the bubbles increases from 2cm to 5cm, the peak shifts by 0.4ns, and the echo pulse width increases by 0.6ns. The simulation model and experimental results provide an effective basis for distinguishing ship wakes and strong backscattering targets, and have an important role in improving wake detection and recognition capabilities.
Aiming at the problem that the Geiger-mode Avalanche Photodiode (GM-APD) is susceptible to background noise and the detection effect decreases during the day, based on the polarization GM-APD detection model, a set of GM-APDbased polarized lidar imaging experimental equipment is proposed. Using this equipment to image the target in the simulated sunlight environment, the effect of polarization detection on the echo triggering performance was studied. The results show that, compared with the non-polarized system, the polarized system reduces the impact of noise on target detection, improves the image quality, and reduces the false alarm probability of the image. When the laser single pulse energy is 400nJ, and the polarization angle is 135° , the trigger probability of the metal plate is increased by 10.5%, and its false alarm probability is reduced by 4.6%; the trigger probability of the rough wood board is reduced by 15.2%, and its false alarm probability is reduced 8.9%.Due to the deterioration of imaging results caused by background noise, it is proposed to use polarization degree images for further background filtering to extract a more complete target contour. This research work provides experimental support for the effective detection of GM-APD lidar during the day.
Geiger-mode Avalanche Photo Diode(Gm-APD) Ladar is a probabilistic device outputting three dimensional(3D) images based on the multi-frame imaging statistics, which makes the 3D recovery algorithm one of the key techniques of imaging system. Besides, performance of algorithm plays a crucial role in improving recovery quality of 3D images. This paper researches three 3D algorithms based on histograms, containing peak-selecting algorithm, range-selecting algorithm and Gaussian fitting algorithm. Firstly, the triggered model of Gm-APD is analyzed based on the work timing sequence and imaging theory of Gm-APD Ladar. Meanwhile, the recovery principles of three algorithms are analyzed and clarified. Secondly, two evaluation criterions, average range error and accuracy of range recovery, are raised to evaluate range accuracy. Finally, range images are obtained with above three algorithms in statistics of different frames, based on original data obtained from 64 × 64 Gm-APD Ladar imaging experiment. With the three construction algorithms, the result shows that the range accuracy of recovery range images improves and converges to 0.2~0.3m with the increment of number of frames participating in the statistics, and the accuracy of range recovery can be up to 90%. In low frame numbers, the range accuracy of recovery range profile is worst with peak-selecting algorithm, and the average range error with rangeselecting algorithm performs best while accuracy of range recovery with Gaussian fitting algorithm is highest among all algorithms. The result has important guiding significance for the choice of recovery algorithm under different requests.
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