Unmanned aerial vehicle remote sensing image matching techniques are crucial for urban planning and environmental monitoring. However, existing feature matching methods have limitations due to the lack of effective extraction of key diverse features, which hinders the improvement of matching accuracy for diverse ground scenes. To address the above limitations, we propose a remote sensing image feature-matching method using optimally regularized nonlinear diffusion model. Specifically, we first use the accelerated-KAZE (AKAZE) algorithm framework to detect and characterize features within a nonlinear scale space. To improve the adaptation of diffusion to the local image structure, we propose the introduction of a novel Charbonnier regularized nonlinear diffusion model for constructing the diffusion coefficients. We then propose to combine Brute force with the K-nearest neighbor algorithm to eliminate invalid matches. Lastly, we modified the sampling approach of the random sample consensus (RANSAC) algorithm. Instead, we achieve iterative dataset purification using the progressive sample consensus algorithm, complemented by the implementation of a termination criterion for the sampling process. Experimental results show that our method achieves an average matching precision of 82.8%, 80.2%, 82.9%, and 91.2% when the test set undergoes scale, rotation angle, noise blurring, and luminance variations, respectively, outperforming the existing feature matching methods.
Laser measurement system has the advantages of fast speed, high precision, and cost-effective. But the laser itself coherently produces speckles, the presence of laser speckle can seriously degrade image quality, leading to decrease in image recognition accuracy, thus reducing the accuracy of measurement. In this paper, we propose a multi-beam superposition method to reduce laser speckle. We use four lasers with equal optical power as the incident light source, then build the entire system light path by beam shaping and polarized beams superimposing, which achieves the line structured light required for the laser measurement system. According to the theoretical analysis and simulation optimization, the speckle contrast was reduced to 50% of the original value in this way. After beam shaping, we can obtain the linear laser beam with a line width of less than 1mm, a field of view angle of 66.8°, a light energy loss of less than 10% and an energy uniformity of 97.25% at a projection distance of 1000mm.The above results highlight a viable approach to decrease speckle contrast and measurement errors. This system can achieve high power and low speckle contrast light sources in the field of measurement, with outstanding result in practicability and convenience. On the other hand, this system can effectively solve the problems of serious error and low efficiency in measurement.
The need for imaging and detecting in various instrument areas has brought light detection and ranging (LiDAR) to the forefront of consumer technology. Among different LiDAR, microelectromechanical system (MEMS) LiDAR has more appeal due to its small size and high integration. However, due to its low resolution and slight scanning angle, MEMS LiDAR does not apply to all scenes. Thus we presented a 360-deg scanning LiDAR emission optical system. Design formulas were deduced from theoretical formulas. In this system, the aspheric lens collimated the beam, MEMS micromirrors tracked the concentric circular beams, and the converging lens compensated for the divergence of the outgoing beam. By doing so, the target was scanned 360 deg at high resolution. With Zemax, the fast-axis divergence angle was 0.2484 mrad, the slow-axis divergence angle was 0.1546 mrad, and the system energy utilization rate was 84.16% with an angular resolution of 0.0142 deg at a distance of 10,000 mm. We have provided a potential solution for improving the scanning angle and resolution of MEMS LiDAR.
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