Session WedAT1 Pegaso A Wednesday, October 10, 2012 ... - Lirmm

Session WedAT6 Gemini 3 Wednesday, October 10, 2012, 08:30–09:30
Mapping I
Chair
Co-Chair
08:30–08:45 WedAT6.1
IPJC: The Incremental Posterior Joint
Compatibility Test for Fast Feature Cloud
Matching
Yangming Li
Institute of Intelligence Machines, Chinese Academy of Sciences, China
Edwin Olson
Computer Science and Engineering, University of Michigan, USA
• We propose a new
probabilistic data association
method for feature clouds.
• Dramatically faster than
JCBB, while mathematically
equivalent in linear case.
• Better false positive/true
positive performance than
JCBB in non-linear case.
09:00–09:15 WedAT6.3
Patch Map: A Benchmark for Occupancy Grid
Algorithm Evaluation
Rehman S. Merali and Timothy D. Barfoot
University of Toronto Institute for Aerospace Studies, Canada
• Traditional occupancy grid (OG) mapping
makes two assumptions for computational
efficiency
• We present the full Bayesian solution for
OG mapping, which makes no assumptions
• The full solution cannot be computed for
realistic 2D (or 3D) maps, so we introduce
(a) Traditional occupancy grid mapping
a novel patch map algorithm
• The patch map is shown to approximate the
full solution in a simple 1D test case,
whereas traditional OG mapping does not
• The patch map is shown to work on realistic
2D data, where the full solution cannot be
computed
• The patch map is a suitable benchmark to
quantify/optimize future online OG mapping (b) Patch map algorithm
algorithms
Patch map algorithm better approximates
the true information in the map.
08:45–09:00 WedAT6.2
Fast Incremental Clustering and Representation
of a 3D Point Cloud Sequence with Planar Regions
Francesco Donnarumma
Istituto di Scienze e Tecnologie della Cognizione, CNR, Italy
Vincenzo Lippiello
Dipartimento di Informatica e Sistemistica,
Università degli Studi di Napoli Federico II, Italy
Matteo Saveriano
Institute of Automatic Control Engineering,
Technische Universität München, Germany
• An incremental clustering technique to
partition 3D points into planar regions is
presented
• Incremental PCA and a compact
geometrical representation (concavehull)
for computational efficiency
• The algorithm works in real-time on
unknown and noisy data
• Validated both on synthetic and real
(interior of a building) datasets
09:15–09:30 WedAT6.4
Independent Markov Chain Occupancy Grid Maps
for Representation of Dynamic Environments
Jari Saarinen
Automation and Systems Technology, Aalto University, Finland
Henrik Andreasson, Achim J. Lilienthal
Center of Applied Autonomous Sensor Systems, Örebro University, Sweden
� Each cell is an independent Markov chain
(iMac)
• The state transition parameters are
modeled as two Poisson processes
• Online learning of parameters
• Model estimates both the expected
occupancy as well as behavior of
dynamics on a cell level (static, dynamic
and shades of semi-static)
• Approach is evaluated with a long-term
dataset taken from an AGV in production
use.
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
–124–
Evolution of model parameters