Reliable Real-time Change Detection and Mapping for 3D LiDARs
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Author / Producer
Date
2017
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
A common scenario in Search and Rescue robotics
is to map and patrol a disaster site to assess the situation and
plan potential missions of rescue teams. Particular importance
has to be given to changes in the environment as these may
correspond to critical events like building collapses, movement
of objects, etc. This paper presents a change detection pipeline
for LiDAR-equipped robots to assist humans in detecting those
changes. The local 3D point cloud data is compared to an
octree-based occupancy map representation of the environment
by computing the Mahalanobis distance to the closest voxel in
the map. The thresholded distance is processed by a clustering
algorithm to obtain a set of change candidates. Finally, outliers
in these sets are filtered using a random forest classifier.
Changes are continuously mapped during a sortie based on
their classification score and number of occurrences. Changes
are reported in real time during robot operation.
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Publication status
published
External links
Editor
Book title
2017 IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR)
Journal / series
Volume
Pages / Article No.
81 - 87
Publisher
IEEE
Event
2017 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
09570 - Hutter, Marco / Hutter, Marco
03737 - Siegwart, Roland Y. / Siegwart, Roland Y.
Notes
Funding
609763 - Long-Term Human-Robot Teaming for Robot-Assisted Disaster Response (EC)