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

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

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 m...

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Bibliographic Details
Published in:2020 IEEE International Conference on Robotics and Automation (ICRA) pp. 9673 - 9679
Main Authors: Gomez, Clara, Fehr, Marius, Millane, Alex, Hernandez, Alejandra C., Nieto, Juan, Barber, Ramon, Siegwart, Roland
Format: Conference Proceeding
Language:English
Published: IEEE 01-05-2020
Subjects:
Indoor environments
Measurement
Path planning
Robots
Semantics
Three-dimensional displays
Two dimensional displays
Online Access:Get full text
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Summary: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.
ISSN:2577-087X
DOI:10.1109/ICRA40945.2020.9197226