Learning Topometric Semantic Maps from Occupancy Grids
- URL: http://arxiv.org/abs/2001.03676v1
- Date: Fri, 10 Jan 2020 22:06:10 GMT
- Title: Learning Topometric Semantic Maps from Occupancy Grids
- Authors: Markus Hiller, Chen Qiu, Florian Particke, Christian Hofmann and
J\"orn Thielecke
- Abstract summary: We propose a new approach for deriving such instance-based semantic maps purely from occupancy grids.
We employ a combination of deep learning techniques to detect, segment and extract door hypotheses from a random-sized map.
We evaluate our approach on several publicly available real-world data sets.
- Score: 2.5234065536725963
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Today's mobile robots are expected to operate in complex environments they
share with humans. To allow intuitive human-robot collaboration, robots require
a human-like understanding of their surroundings in terms of semantically
classified instances. In this paper, we propose a new approach for deriving
such instance-based semantic maps purely from occupancy grids. We employ a
combination of deep learning techniques to detect, segment and extract door
hypotheses from a random-sized map. The extraction is followed by a
post-processing chain to further increase the accuracy of our approach, as well
as place categorization for the three classes room, door and corridor. All
detected and classified entities are described as instances specified in a
common coordinate system, while a topological map is derived to capture their
spatial links. To train our two neural networks used for detection and map
segmentation, we contribute a simulator that automatically creates and
annotates the required training data. We further provide insight into which
features are learned to detect doorways, and how the simulated training data
can be augmented to train networks for the direct application on real-world
grid maps. We evaluate our approach on several publicly available real-world
data sets. Even though the used networks are solely trained on simulated data,
our approach demonstrates high robustness and effectiveness in various
real-world indoor environments.
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