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This paper addresses challenges in 3D model construction and path planning using UAV oblique photogrammetry, specifically image registration and optimization algorithm performance. It proposes a point set affine registration method with control point consistency constraints to improve image registration accuracy. The paper also introduces an adaptive chaotic particle swarm optimization (SACPSO) algorithm to mitigate the issue of iterative algorithms getting trapped in local optima during path planning.
Control-point consistency and adaptive chaotic optimization combine to enable more robust 3D model construction from UAV photogrammetry.
The drone oblique photogrammetry technology has been widely used in military and civilian fields due to its advanced positioning technology, rich image information, and accurate geographic information. This article proposes solutions based on the “point set affine registration (ICP) with control point consistency constraints[1]” and “adaptive chaotic particle swarm optimization (SACPSO)[2]” algorithms to address the problems of “extremely difficult image point set registration” and “common iterative algorithm population easily falling into local optima” when using unmanned aerial vehicle (UAV) oblique photogrammetry technology for high simulation real-world 3D model construction and path planning.
