DCEGen: Dense clutter environment generation tool for autonomous 3D exploration and coverage algorithms testing

Abstract

© Springer Nature Switzerland AG 2019. Autonomous exploration and coverage in 3D environments recently has became a rapidly developing research field. Emerging 3D reconstruction methods, designed specifically for exploration and coverage, allows capturing an environment in a greater details. However, not much work addresses certain difficulties inherent to dense clutter environments. We observed those difficulties and made an attempt that seeks to expand the applicability of such methods to more demanding scenarios. Automating the process of testing and evaluation by designing a dense clutter environment generation algorithm (DCEGen) allows us to measure comparative performance of available algorithms. We focus on path-planning algorithms used in an unmanned ground vehicles. The algorithm was implemented and verified using Gazebo simulator.