Abstract
This paper introduces a genetic algorithm (GA) and a beam tracing algorithm incorporated within a dual parallel computing framework to optimize urban aerial laser scanning (ALS) missions to maximize vertical façade data capture, as needed for many three-dimensional reconstruction and modeling workflows. The optimization employs a low-density point cloud from the site of interest as a spatial representation of the urban scene. The GA is suitable for LiDAR flight path optimization due to its capability of handling open-ended problems that have many solutions. However, GAs require evaluating a very large number of candidates. The use of an initial point cloud allows realistic modeling of the urban environment in the optimization at the cost of high data input volumes. To cope with the computational and data demands, a dual parallel computing framework was devised. The parallel computing framework consists of two layers of parallelization. In the upper layer, multiple evaluators work in parallel and in conjunction with a main multi-threading GA optimizer to perform GA operations and evaluate the flight paths. In the lower layer, to evaluate assigned flight paths, each evaluator distributes its data and computation to multiple executors, which can reside on multiple physical nodes of a distributed-memory computing cluster. In addition to parallelism, the data partitioning on the lower layer allows out-of-core computation. Namely, data partitions are efficiently transferred between disks and memory so that only relevant subsets of data are kept in the main memory. The objective of the proposed method is threefold: (1) search for flight paths that yield the highest numbers of vertical points, (2) create a means to explicitly consider the detailed spatial configuration of urban environments, and (3) assure that the proposed optimization strategy is fast and can scale to large problem sizes. Multiple experiments were conducted and demonstrated the success of the proposed method. Converged results were achieved after dozens of generations within two hours. Two flight paths identified by the GA as the most and the least optimal candidates were deployed in real flight missions. The optimal flight path captured 16% more vertical points than the least optimal one, slightly higher than the 13% predicted. Both layers of parallelization were efficient: 13.1/16 for the lower layer and 3.2/4 for the upper layer. The two complementary layers of parallelization allowed flexible and efficient use of distributed computing resources to reduce the runtime. The scalability of the proposed approach was successfully demonstrated up to a data size of 460 million points. The optimization results were realistic and aligned well with the test flight results.
Highlights
Introduction distributed under the terms andLight detection and ranging (LiDAR) is a technology that uses light, most commonly from a laser, to detect and measure distance to objects
With respect to urban mapping, the manual recommends an increase in the overlap between adjacent flight swaths to account for the presence of tall buildings or other tall features in the urban environment, but no consideration is made for vertical data capture, as the guidelines were written for topographic mapping to map the ground surface between the buildings and not the buildings themselves
To confirm the success of the genetic algorithm (GA) in LiDAR flight path optimization, the GA was executed with two opposing objectives: maximizing and minimizing the fitness function
Summary
Introduction distributed under the terms andLight detection and ranging (LiDAR) is a technology that uses light, most commonly from a laser, to detect and measure distance to objects. LiDAR sensors can be deployed on an aerial platform (e.g., fixed wing aircraft, helicopter, or drone) to form an aerial laser scanning (ALS) system for topographic, biomass, or urban mapping. Typical ALS missions result in 0.5–10 points/m2 , which is standard for topographic mapping [4]. LiDAR flight mission planning is the process of determining sensor settings (e.g., scan angle, scan frequency, pulse rate) and flight settings (e.g., platform, flight altitude, speed, flight map), among other parameters to deliver project requirements. Common requirements include LiDAR point density, distribution, and accuracy. The ASPRS’s Manual of Airborne Topographic LiDAR [20] provides guidelines for LiDAR flight mission planning.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
