DS-K3DOM: 3-D Dynamic Occupancy Mapping with Kernel Inference and Dempster-Shafer Evidential Theory

TL;DR

This paper introduces DS-K3DOM, a real-time 3-D dynamic occupancy mapping algorithm for aerial robots, combining Bayesian updates, Dempster-Shafer theory, and kernel inference for dense mapping from sparse data.

Contribution

The paper proposes a novel 3-D dynamic occupancy mapping method that integrates Bayesian, Dempster-Shafer, and kernel inference techniques for real-time aerial robot applications.

Findings

Effective in simulations and real experiments

Enables dense 3-D mapping from sparse measurements

Supports real-time processing for aerial navigation

Abstract

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.