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.
To register partially overlapping three-dimensional point sets from different viewpoints, it is necessary to remove spurious corresponding point pairs that are not located in overlapping regions. Most variants of the iterative closest point (ICP) algorithm require users to manually select the rejection parameters for discarding spurious point pairs between the registering views. This requirement often results in unreliable and inaccurate registration. To overcome this problem, we present an improved ICP algorithm that can automatically determine the rejection percentage to reliably and accurately align partially overlapping laser-radar (ladar) range images. The similarity of k neighboring features of each nonplanar point is employed to determine reasonable point pairs in nonplanar regions, and the distance measurement method is used to find reasonable point pairs in planar regions. The rejection percentage can be obtained from these two sets of reasonable pairs. The performance of our algorithm is compared with that of five other algorithms using various models with low and high curvatures. The experimental results show that our algorithm is more accurate and robust than the other algorithms.
In order to improve the measurement accuracy of the angle and signal processing speed of operation, this paper proposes a novel method of second harmonic measurement of multi-beam laser heterodyne for the angle, which based on the combination of Doppler effect and heterodyne technology, loaded the information of the angle to the frequency difference of second harmonic of the multi-beam laser heterodyne signal by frequency modulation of the oscillating mirror, which is in the light path. Heterodyne signal frequency can be obtained by fast Fourier transform, and can obtain values of the angle accurately after the multi-beam laser heterodyne signal demodulation. This novel method is used to simulate measurement for incident angle of standard mirror by Matlab, the obtained result shows that the relative measurement error of this method is just 0.5213%.
This paper proposes a novel method of multi-beam laser heterodyne measurement for Young modulus. Based on Doppler effect and heterodyne technology, loaded the information of length variation to the frequency difference of the multi-beam laser heterodyne signal by the frequency modulation of the oscillating mirror, this method can obtain many values of length variation caused by mass variation after the multi-beam laser heterodyne signal demodulation simultaneously. Processing these values by weighted-average, it can obtain length variation accurately, and eventually obtain value of Young modulus of the sample by the calculation. This novel method is used to simulate measurement for Young modulus of wire under different mass by MATLAB, the obtained result shows that the relative measurement error of this method is just 0.3%.
Laser radar can simultaneously produce the range image and the intensity image, and it can directly collect rich information of target. Compared with the other sensors, such as infrared or radar, laser radar can enhance the recognition rate and the precision of target aimed point. When laser radar vertically detects the objects on the plane ground, the correlation filters with in-plane rotation invariance are usually used to solving the problem of the target recognition. Traditional correlation filters are still improved on the aspect of recognition rate. In the paper, through deducing the relationship between support vector machine (SVM) and correlation principle in the signal processing, a new correlation filter, named linear SVM correlation filter (LSCF) that has the properties of SVM, is proposed. The real images of laser radar are used as the training and testing samples. The experiments state that the filter has good recognition attributes, such as stable correlation output and high recognition rate. LSCF is suitable to be the recognition algorithm of the imaging laser radar.
Laser radar can simultaneously produce the intensity and range images, and the space resolution is high, so the
recognition performance is well, and it can choose the aim point of target. Laser radar is applied to many fields, such as
guidance, navigation, and becomes the research hot point in recent years. In the vertical detection of laser radar, the
algorithm is required not only solving in-plane rotation-invariant problem, also the distortion-invariant problem, and it
must satisfied the real-time. Correlation algorithm is a parallel processing procedure, detecting many targets at one time,
and its design can be implemented on the high speed digital signal processor. In the paper, a new filter named
CHF-MACH filter is presented, which combine multiple circular harmonic expansions into one filter through MACH
criteria. Because of the filter having the characters of the two filters, it can solve the problems of in-plane
rotation-invariance and distortion-invariance simultaneously, and meet the real-time requirement. The simulated range
image of laser radar is regarded as research target, and computing the PSR (peak to sidelobe ratio) values of correlation
output of the different objects, and plotting the PSR curves of the different angles. Simulating the scene of laser radar
which includes multiple objects, CHF-MACH filter performance is validated through testing with the different angles for
the objects, and the non-training images can obtain the well correlation output.
The target recognition of laser radar is a hot research because laser radar can produce the intensity and range imagery. Laser radar has high space resolution, and can obtain rich target information. Correlation recognition has been used to many fields, such as infrared as well as synthetic aperture radar (SAR). In this paper, the two filters are used in experiment of laser radar. MACH filter is used to detect the target, and DCCFs are used to recognize the unknown target. The samples are generated by OpenGL technology, and the filters are designed using the simulated ladar images. The test samples are added noise according to the imaging principle of laser radar. Two sample sets, one adding noise, another filtering the noise, are used in order to contrast the different performance. At last, the experiment results are given.
Intensity, range and Doppler images can be obtained by using laser radar. Laser radar can detect much more object information than other detecting sensor, such as passive infrared imaging and synthetic aperture radar (SAR), so it is well suited as the sensor of object recognition. Traditional method of laser radar object recognition is extracting target features, which can be influenced by noise. In this paper, a laser radar recognition method-Support Vector Machine is introduced. Support Vector Machine (SVM) is a new hotspot of recognition research after neural network. It has well performance on digital written and face recognition. Two series experiments about SVM designed for preprocessing and non-preprocessing samples are performed by real laser radar images, and the experiments results are compared.
There has been much interest in the field of laser radar (ladar) owing to its high-resolution three-dimensional imagery. However, the coherent ladar images are affected badly by speckle, which is a multiplicative noise and has the statistical features of the negative exponential density. In the paper, the morphological filter based on the parametric edge detection is introduced in detail. Then, this detector and the ratio edge detector are compared with the conventional LOG (Laplacian of Gaussian) operator and Canny operator. At last, the edge detection results for coherent laser radar image are obtained. The experimental results show that the morphological filter based on parametric edge detection and the ratio edge detector are better than other algorithms for coherent ladar image, and the morphological filter based on the parametric edge detection is the best algorithm in this paper.