论文标题
DS-K3DOR：3-D动态占用映射与内核推理和Dempster-Shafer证据理论
DS-K3DOM: 3-D Dynamic Occupancy Mapping with Kernel Inference and Dempster-Shafer Evidential Theory
论文作者
论文摘要
占用映射已被广泛用于代表自动驾驶机器人的周围环境，以执行导航和操纵等任务。虽然在2D环境中进行了充分研究，但几乎没有适用于3-D动态占用映射的方法，这对于空中机器人必不可少。本文介绍了一种新型的3-D动态占用映射算法，称为DS-K3Dom。我们首先建立了一种贝叶斯方法，以根据随机有限集理论进行依次更新占用图的测量流。然后，我们用Dempster-shafer域中的粒子近似它，以实现实时计算。此外，该算法将基于内核的推理与Dirichlet基本信念分配一起使用，以从稀疏测量中实现密集的映射。通过模拟和实际实验证明了所提出算法的功效。
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.
