论文标题
图形增益：机器人探索的基于凹形壳的体积增益
Graph Gain: A Concave-Hull Based Volumetric Gain for Robotic Exploration
论文作者
论文摘要
机器人探索的现有体积增益是在3D占用图中计算出来的，而基于抽样的探索方法则在可及（免费）空间中扩展。它们之间的不一致使得体积增益的现有计算不适合对环境进行完整的探索。为了解决这个问题，我们在基于抽样的探索框架中提出了一个基于凹形的体积增益。凹面船体是根据快速探索随机树（RRT）和无法展开的节点生成的观点来构建的。该凹面船体以外的所有空间都被认为是未知的。体积增益是根据观点配置而不是使用占用图来计算的。有了新的体积增益，机器人可以避免由于现有体积增益计算方法的不适当性而导致的效率低下甚至错误的勘探行为。我们的探索方法是根据基准环境中现有的基于RRT的现有方法评估的。在评估的环境中，我们方法的平均运行时间约为现有最新方法的38.4％，我们的方法更强大。
The existing volumetric gain for robotic exploration is calculated in the 3D occupancy map, while the sampling-based exploration method is extended in the reachable (free) space. The inconsistency between them makes the existing calculation of volumetric gain inappropriate for a complete exploration of the environment. To address this issue, we propose a concave-hull based volumetric gain in a sampling-based exploration framework. The concave hull is constructed based on the viewpoints generated by Rapidly-exploring Random Tree (RRT) and the nodes that fail to expand. All space outside this concave hull is considered unknown. The volumetric gain is calculated based on the viewpoints configuration rather than using the occupancy map. With the new volumetric gain, robots can avoid inefficient or even erroneous exploration behavior caused by the inappropriateness of existing volumetric gain calculation methods. Our exploration method is evaluated against the existing state-of-the-art RRT-based method in a benchmark environment. In the evaluated environment, the average running time of our method is about 38.4% of the existing state-of-the-art method and our method is more robust.
