Hybrid Topological and 3D Dense Mapping through Autonomous Exploration for Large Indoor Environments


METADATA ONLY
Loading...

Date

2020

Publication Type

Conference Paper

ETH Bibliography

yes

Citations

Altmetric
METADATA ONLY

Data

Rights / License

Abstract

Robots require a detailed understanding of the 3D structure of the environment for autonomous navigation and path planning. A popular approach is to represent the environment using metric, dense 3D maps such as 3D occupancy grids. However, in large environments the computational power required for most state-of-the-art 3D dense mapping systems is compromising precision and real-time capability. In this work, we propose a novel mapping method that is able to build and maintain 3D dense representations for large indoor environments using standard CPUs. Topological global representations and 3D dense submaps are maintained as hybrid global map. Submaps are generated for every new visited place. A place (room) is identified as an isolated part of the environment connected to other parts through transit areas (doors). This semantic partitioning of the environment allows for a more efficient mapping and path-planning. We also propose a method for autonomous exploration that directly builds the hybrid representation in real time.We validate the real-time performance of our hybrid system on simulated and real environments regarding mapping and path-planning. The improvement in execution time and memory requirements upholds the contribution of the proposed work. © 2020 IEEE.

Publication status

published

Editor

Book title

2020 IEEE International Conference on Robotics and Automation (ICRA)

Journal / series

Volume

Pages / Article No.

9673 - 9679

Publisher

IEEE

Event

IEEE International Conference on Robotics and Automation (ICRA 2020)

Edition / version

Methods

Software

Geographic location

Date collected

Date created

Subject

Organisational unit

03737 - Siegwart, Roland Y. / Siegwart, Roland Y. check_circle

Notes

Due to the Coronavirus (COVID-19) the conference was conducted virtually.

Funding

Related publications and datasets