Automatic detection using radar data of airborne laser pointer

Non-scanning lidar is a second-generation, compact, real-time air-cooled instrument. According to the extensive ground characterization within the flight range, the non-scanning lidar can image obstacles with a tilt range of 1 km, with an accuracy ranging from 8 cm and an accuracy range better than 35 cm, all within 1-σ. The non-scanning lidar can identify landing obstacles as small as 30 cm, and the maximum tilt range that the Morpheus project can achieve is 450 meters.

However, under certain wind conditions, it is prone to flicker caused by the rocket engine heating the air and pre-triggered by dust clouds created during the launch and the wind-induced transmission of the test flight direction. A quick understanding of the road network is essential to formulate an efficient emergency plan after any major disaster. Falling buildings, stationary vehicles, and other forms of debris often make the road impassable for emergency personnel. The state of the road is generally determined through time-consuming and labor-intensive methods, such as field surveys and manual interpretation of remote sensing images.

The airborne lidar system provides a low-cost alternative for performing road network assessments. 3D data in a wide range can be quickly collected to provide valuable information about the geometry and structure of the scene. This paper presents a method to automatically detect and characterize road debris using airborne laser pointer radar data. The points that fall within the range of the road are extracted from the point cloud and aggregated into a single object using area growth.

RGB High Tech Handheld Laser Pointer

Using surface properties and contextual clues, objects are classified as fragmented or non-fragmented. The debris pile uses the alpha shape to reconstruct the surface, so that the volume of the debris can be estimated. The paper gives the results of real-time lidar data collected after natural disasters. The preliminary results show that accurate debris maps can be automatically generated using the proposed method. Despite the incomplete transportation network, these fragmented maps will be a very valuable asset for disaster management and emergency teams trying to find survivors.

For data with high aspect ratio geometric dimensions and running on curve coordinates, a method for automatically recording lidar data sets is specially customized. Relaxed the minimum change between the typical adjacent data sets required by the angle in other algorithms. The range of data is filtered with a series of discrete Gaussian and Gaussian filters to form a second-order Taylor series that approximates the surface of each sampling point. The principal curvature relative to the surface normal is calculated and compared across adjacent data sets to determine the homology and the most suitable transition matrix. This method reduces the requirements for the amount of raw data and processing time.

Small surface targets and airborne targets have increasing demands. Due to the requirement of very high lateral sensor resolution, the long-range ID or shorter-range ID of small targets has its limitations in imaging. This has also stimulated the need to develop one-dimensional green laser pointer technology for the target ID. These measures include vibration measurement, and laser ranging analysis. Vibration measurement can get good results, but it is also sensitive to specific vibrating parts on the target in the field of view. Laser ranging analysis is attractive because the maximum range can be very large, especially when the laser beam width is small.

The distance analyzer can also be used in scanning mode to detect targets in a certain sector. When the target is closer and angularly resolved, the same laser can also be used for active imaging. This paper will show the experimental and simulation results of laser ranging analysis of small ships within 6-7 kilometers and short-range (1.3 kilometers) UAV prototypes. Using this analysis system, the author has achieved good results in target detection and recognition at the same time. The CAD model of the target is used to compare the waveforms of the experimental and simulated ranging, which supports the idea of ​​the analysis system as the first recognition sensor, thereby narrowing the search space for automatic target recognition based on imaging in close range.