Flight Optimization Algorithms for Aerial LiDAR Capture for Urban Infrastructure Model Generation

Abstract

Aerial light detection and ranging (LiDAR) offers the potential to autogenerate detailed, three-dimensional (3D) models of the built environment in urban settings. Autogeneration is needed as manual generation is not economically feasible for large areas, and such models are needed for a wide range of applications from improved noise and pollution prediction to disaster mitigation modeling and visualization. Current laser scanning hardware and the dense geometry of urban environments are two major constraints in LiDAR scanning. This paper outlines the difficulties related to effective surface data capture, with emphasis on vertical surfaces, in an urban environment for the purpose of 3D modeling. A flight planning strategy to overcome these difficulties is presented, along with a case study of a data set collected with this strategy. The main conclusions of this study are that an appropriate amount of strip overlap, together with a flight path diagonal to the underlying street grid produces a vastly enhanced level of detail on vertical surfaces, beyond what has been previously available.