The computer science part of the DP-SLAM
project
involves the algorithms and analysis to show that these data structures
can
be maintained efficiently. Please see the
documents
section for more details.
The original DP-SLAM paper (DP-SLAM 1.0) was presented at
IJCAI 03 and was
based
upon an assumption that the environment behaved deterministically with
respect
to the robot's laser range finder. The original paper is
available
here.
Since the original paper was published, we have improved our algorithm
and
our analysis. We also developed a novel model of how the laser
penetrates
space, which enables us to achieve a more robust algorithm that
produces
maps of extraordinary accuracy using probabilistic occupancy. We
refer
to these improvements together as
DP-SLAM 2.0.
Our latest development with DP-SLAM now permits run time that is linear
in all relevant parameters of the problem. For P particles and
area A swept out by the laser, the run time is O(AP) per iteration of
the particle filter. This is the same complexity per particle as
pure localization! We have also implemented a hierarchical
approach that combats drift in our particle filter. Our
NIPS 05 paper on
these improvements is available.
Austin Eliazar's doctoral dissertation
is now available.
We have run DP-SLAM on two building environments. The first is
from
the second floor the Duke University Computer Science Department, i.e.,
the
second floor of the D-Wing of the LSRC building. The second is
from
a long stretch of the second floor of the C-Wing of LSRC
(pharmacology).
Clicking on the maps below will bring you to a web page with detailed
on the
performance of DP-SLAM (both versions) in each domain. We include
results
with different algorithm parameters and log files.
The maps shown below are at a tiny fraction of the actual map
resolution.
Please click on the
maps to see the full resolution, highly detailed versions.
|
|
D Wing
(Click for Details)
|
C Wing
(Click for Details)
|
Dieter Fox
and
Dirk
Haehnel were kind enough to provide us with a quite large data set
from Wean hall at
CMU. This
one actually contains several loops, only one of which is show in our
draft paper.
|
Wean Hall
(Click for Details)
|
We are pleased to make a version 0.1.1 release of the DP-SLAM code
available to the research community. This version has been
developed and tested under Linux. It can be used "live" on an
iRobot with a laser range finder. Otherwise, it can be used to
process a log file. It should be fairly straightforward to modify
this code to work with log files in different formats or to operate
"live" on different platforms.
Please note that your use of this code is subject to the following
conditions
This Program is provided by Duke University and the
authors as a service to the research community. It is provided without
cost or restrictions, except for the User's acknowledgment that the
Program is provided on an "As Is" basis and User understands that Duke
University and the authors make no express or implied warranty of any
kind. Duke University and the authors specifically disclaim any
implied
warranty or merchantability or fitness for a particular purpose, and
make no representations or warranties that the Program will not
infringe
the intellectual property rights of others. The User agrees to
indemnify
and hold harmless Duke University and the authors from and against any
and all liability arising out of User's use of the Program.
If you agree to these conditions, you may download:
dpslam0.1.1.tar.gz
Old Version:
dpslam0.1.tar.gz
We are grateful for the support of the
National
Science Foundation, the
Alfred P.
Sloan Foundation, and
SAIC for
this project.