A dangerous area prediction method based on lidar data

Abstract

The invention discloses a laser radar data based dangerous zone anticipation method. The method includes the following steps: S1, collecting pole and tower information within a grid line range by utilizing laser radar equipment, and outputting the three-dimensional point cloud data of the pole and tower information; S2, processing the pole and tower information, marking barrier in the tour inspection route of an unmanned aerial vehicle, and generating a cuboid dangerous zone; S3, constructing a local ground coordinate system, and giving the conversion relation between space positions and coordinate points in the coordinate system; S4, converting the eight corner points of the cuboid dangerous zone into the local ground coordinate system so that coordinates under the local ground coordinatesystem can be obtained; S5, converting the space position where the unmanned aerial vehicle is located into the local ground coordinate system so that the coordinates of the unmanned aerial vehicle in the local ground coordinate system can be obtained; and S6, judging whether the unmanned aerial vehicle is in the dangerous zone. The method can anticipate whether the unmanned aerial vehicle entersthe dangerous zone during flying, so that the possibility of crashing accidents can be reduced; and the method has advantages of being high in anticipation precision, and simple and convenient in judging process.

Description

technical field [0001] The invention relates to unmanned aerial vehicle inspection, in particular to a dangerous area prediction method based on laser radar data in the inspection process. Background technique [0002] With the widespread popularity of UAV applications, autonomous or semi-autonomous flight of UAVs needs to be realized in many applications, which requires presetting flight routes for UAVs or requiring UAVs to have a certain function of autonomously planning routes. In actual flight, especially when the UAV flight area is close to the ground or there are many obstacles in the application scene, planning the route is a very tedious task, and collision accidents may occur if you do not pay attention. [0003] The current solution is to set up a local no-fly zone based on the 3D map. In the case where the 3D map cannot meet the requirements, it can only be measured manually and then set the no-fly zone. These methods have problems such as low precision and cumber...

AI Technical Summary

Problems solved by technology

In actual flight, especially when the UAV flight area is close to the ground or there are many obstacles in the application scene, planning the route is a very tedious task, and collision accidents may occur if you do not pay attention

[0003] The current solution is to set up a local no-fly zone based on the 3D map. In the case where the 3D map cannot satisfy the situation, it can only be measured manually and then set the no-fly zone; these methods have problems such as low precision and cumbersome process.

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