DS-K3DOM: 3-D Dynamic Occupancy Mapping with Kernel Inference and Dempster-Shafer Evidential Theory
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.
- Publication:
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arXiv e-prints
- Pub Date:
- September 2022
- DOI:
- 10.48550/arXiv.2209.07764
- arXiv:
- arXiv:2209.07764
- Bibcode:
- 2022arXiv220907764H
- Keywords:
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- Computer Science - Robotics;
- J.2
- E-Print:
- 7 pages, 3 figures, Accepted to ICRA 2023