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Publications:
A. Diosi, "Laser Range Finder and Advanced Sonar Based
Simultaneous Localization and Mapping for Mobile Robots", PhD
thesis, Monash University, 2005.
A. Diosi and L. Kleeman, "Laser Scan Matching in Polar
Coordinates with Application to SLAM " Proceedings of 2005
IEEE/RSJ International Conference on Intelligent Robots and Systems
(IROS'05), August, 2005, Edmonton, Canada.
This paper presents a novel method for 2D laser scan matching called
Polar Scan Matching (PSM). The method belongs to the family of point
to point matching approaches. Our method avoids searching for point
associations by simply matching points with the same bearing. This
association rule enables the construction of an algorithm faster than
the iterative closest point (ICP). Firstly the PSM approach is tested
with simulated laser scans. Then the accuracy of our matching
algorithm is evaluated from real laser scans from known relative
positions to establish a ground truth. Furthermore, to demonstrate the
practical usability of the new PSM approach, experimental results from
a Kalman lter implementation of simultaneous localization and mapping
(SLAM) are provided.
A. Diosi and L. Kleeman,
"Scan Matching in Polar Coordinates with Application to SLAM "
Technical Report MECSE-29-2005, Intelligent Robotics Research Centre,
Monash University, 2005.
This report presents a novel method for 2D laser scan matching called
Polar Scan Matching (PSM). The method belongs to the family of point
to point matching approaches. Our method avoids searching for point
associations by simply matching points with the same bearing. This
association rule enables the construction of an algorithm faster than
the iterative closest point (ICP). Firstly the PSM approach is tested
with simulated laser scans. Then the accuracy of our matching
algorithm is evaluated from real laser scans from known relative
positions to establish a ground truth. Furthermore, to demonstrate the
practical usability of the new PSM approach, experimental results from
a Kalman lter implementation of simultaneous localization and mapping
(SLAM) are provided.
A. Diosi, G. Taylor and L. Kleeman, "Interactive SLAM
using Laser and Advanced Sonar ", Proceedings of 2005 IEEE
ICRA, 2005, Barcelona.
This paper presents a novel approach to mapping for mobile robots that
exploits user interaction to semi-autonomously create a labeled map
of the environment. The robot autonomously follows the user and is
provided with a verbal commentary on the current location with
phrases such as Robot, we are in the office . At the same time, a
metric feature map is generated using fusion of laser and advanced
sonar measurements in a Kalman filter based SLAM framework, which is
later used for localization. When mapping is complete, the robot
generates an occupancy grid for use in global task planning. The
occupancy grid is created using a novel laser scan registration
scheme that relies on storing the path of the robot along with
associated local SLAM features during mapping, and later recovering
the path by matching the associated local features to the final SLAM
map. The occupancy grid is segmented into labelled rooms using an
algorithm based on watershed segmentation and integration of the
verbal commentary. Experimental results demonstrate our mobile robot
creating SLAM and segmented occupancy grid maps of rooms along a 70
metre corridor, and then using these maps to navigate between
rooms.
A. Diosi and L. Kleeman,
"Advanced Sonar and Laser Range Finder Fusion for Simultaneous
Localization and Mapping " Proceedings of 2004 IEEE/RSJ
International Conference on Intelligent Robots and Systems (IROS'04), September
28 - October 2, 2004, Sendai, Japan.
Increasing the information content of
measurements can ease some of the problems associated with
simultaneous localization and mapping (SLAM). We present an approach
for combining measurements from a laser range finder with measurements
from an advanced sonar array capable of accurate range and bearing
measurements and edge, corner and plane classification. In our
approach sonar aids laser segmentation, laser aids good sonar point
feature selection and laser and sonar measurements of the same object
are fused. We also present a novel approach for fitting right angle
corners to laser range data, which enables simple error estimation
through the minimization of sum of square range residuals. The results
are then used for SLAM with a mobile robot.
A. Diosi and L. Kleeman, "Uncertainty of Line Segments Extracted from Static SICK PLS Laser ,
Australasian Conference on Robotics and Automation Brisbane Dec
2003.
Knowing the uncertainty of measurements is important for sensor
fusion using Kalman filters. Our sensor of interest is the Sick
PLS101-112 laser range finder. We present an approach for straight
line parameter estimation in polar coordinates that results in a
simple covariance estimate and a worst case systematic line parameter
error model based on a range error model backed up by experimental
data. We show that our random and systematic line error models match
experimental data most of the time. We also demonstrate that
systematic line parameter errors can be larger than the random
ones.
A. Diosi and L. Kleeman. "Uncertainty of line segments extracted from static SICK PLS laser scans",
Technical Report MECSE-26-2003, Intelligent Robotics Research Centre,
Monash University, 2003.
Data fusion using Kalman filters requires reasonably good error
models. Our intention to fuse line segments, corners and edges
obtained from a laser scanner and from advanced sonars provided the
motivation on the investigation of Sick PLS laser scanner range
measurement reliability, and line segment estimation precision. We
present an approach for fitting lines straight in the lasers polar
coordinate system, which enables a simple estimation of line parameter
covariance. We also develop systematic error models for line parameter
estimation. Finally we measure our systematic and random error models
experimentally, and show, that systematic errors can be larger than
random ones.
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Last changed: 14/02/2006