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

Session WedCVT6 Gemini 3 Wednesday, October 10, 2012, 11:00–12:30
SLAM II
Chair Seth Hutchinson, Univ. of Illinois
Co-Chair
11:00–11:15 WedCVT6.1
CurveSLAM: An approach for Vision-based
Navigation without Point Features
Dushyant Rao, Soon-Jo Chung and Seth Hutchinson
University of Illinois at Urbana-Champaign, IL, USA
• Many existing SLAM methods use feature
points without exploiting structure.
• We perform stereo vision-based SLAM
using cubic Bézier curves to represent
landmarks.
• Curve parameters are extracted without
any point-based stereo matching.
• The proposed algorithm can perform
SLAM using only path edges as curve
structures.
11:30–11:45 WedCVT6.3
Realizing, Reversing, Recovering:
Incremental Robust Loop Closing
over time using the iRRR algorithm
Yasir Latif, César Cadena and José Neira
University of Zaragoza, Spain
• We consider the problem of false positive
loop closures that any place recognition
system will eventually provide.
• We propose an incremental algorithm to
realize that the place recognition system
has generated wrong constraints, remove
them if necessary, and recompute the
state estimation.
• We demonstrate the performance of our
algorithm in multiple real cases, in multisession
experiments and compared
against the state of the art in robust backends.
12:00–12:15 WedCVT6.5
Towards Persistent Indoor Localization,
Mapping and Navigation using CAT-Graph
Will Maddern, Michael Milford and Gordon Wyeth
School of EE&CS, Queensland University of Technology, Australia
• We present CAT-Graph, an approach to
topo-metric appearance-based SLAM with
constant computational and memory
requirements in a fixed-size environment.
• Loop closures are calculated using a
particle filter constrained to edges on the
topological graph for fixed computation
time.
• Nodes are pruned using a local
information content metric based on visual
saliency to limit total map size.
• We present results on a 7 day indoor
experiment demonstrating constant
update rate and map size, high recall with
zero false positives and reliable
topological path planning within 20% of the
optimal metric path which improves over
time.
Graphical representation of
continuous topology. Nodes
represent visual observations and
edges store local odometry
11:15–11:30 WedCVT6.2
Seamless Aiding of Inertial-SLAM using Visual
Directional Constraints from a Monocular Vision
Usman Qayyum and Jonghyuk Kim
Research School of Engineering, Australian National University, Australia
• The concept of visual directional
constraint is proposed to resolve
the scale ambiguity problem in
monocular visual-inertial systems
• Direct integration of visual
directional vectors to the inertial
system which enable aiding at high
rates
• 3D map being still used to constrain
the drifts but in a relaxed way.
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
–147–
Fig. 1: Multiple loop aiding architecture.
11:45–12:00 WedCVT6.4
Location and Orientation Estimation with an
Electrosense Robot
Yonatan Silverman, Yang Bai
Mechanical Engineering, Northwestern University, USA
James Snyder and Malcolm A. MacIver
Biomedical Engineering, Northwestern University, USA
• Model uses voltage perturbations as a sensor modality from a
generated electric field
• To solve this RO-SLAM problem, orientation must be estimated from
only orientation dependent range metrics
• We determined the correct state of a robot using experimental data
from an electrosensing robot.
• The estimate of the state improved greatly when the robot rotated as
well as translated.
Electric Field without objects
Electric Field with lateral wall
Electric Field with front wall
12:15–12:20 WedCVT6.6
IEEE/RSJ IROS 2012
Pedestrian Detection in Industrial Environments:
Seeing around corners.
Paulo Borges, Ash Tews, Dave Haddon
Autonomous Systems Lab- ICT Centre - CSIRO
� We propose a safety system which integrates a vision-based offboard
pedestrian tracking subsystem with an onboard localisation and navigation
subsystem.
� This combination enables warnings to be communicated and effectively
extends the vehicle controller’s field of view to include areas that would
otherwise be blind spots.
� A simple flashing light interface in the vehicle cabin provides a clear and
intuitive interface to alert drivers of potential collisions.
� We implemented and tested the proposed solution on an automated
industrial vehicle to verify the applicability for both human drivers and under
autonomous operation.