Occupancy mapping has been widely utilized to represent the surroundings for autonomous robots to perform tasks such as navigation and manipulation. While occupancy mapping in 2-D environments has been well-studied, there have been few approaches suitable for 3-D dynamic occupancy mapping which is essential for aerial robots. This paper presents a novel 3-D dynamic occupancy mapping algorithm called DS-K3DOM. We first establish a Bayesian method to sequentially update occupancy maps for a stream of measurements based on the random finite set theory. Then, we approximate it with particles in the Dempster-Shafer domain to enable real-time computation. Moreover, the algorithm applies kernel-based inference with Dirichlet basic belief assignment to enable dense mapping from sparse measurements. The efficacy of the proposed algorithm is demonstrated through simulations and real experiments.

翻译：占用映射已被广泛用于表示自主机器人的周围环境，以执行导航和操作等任务。尽管二维环境下的占用映射已得到充分研究，但适用于空中机器人的三维动态占用映射方法仍较为少见。本文提出了一种名为DS-K3DOM的新型三维动态占用映射算法。我们首先基于随机有限集理论，建立了一种贝叶斯方法，用于连续更新测量数据流对应的占用地图。随后，我们在德普斯特-谢弗域中采用粒子近似实现实时计算。此外，该算法应用基于狄利克雷基本信念分配的核推断方法，实现从稀疏测量到密集映射的目标。通过仿真与真实实验验证了所提算法的有效性。