(Georgia Institute of Technology, 2015-05)
Choudhary, Siddharth; Indelman, Vadim; Christensen, Henrik I.; Dellaert, Frank
In this paper, we present an information-based
approach to select a reduced number of landmarks and poses
for a robot to localize itself and simultaneously build an
accurate map. We develop an information theoretic algorithm
to efficiently reduce the number of landmarks and poses in
a SLAM estimate without compromising the accuracy of the
estimated trajectory. We also propose an incremental version of
the reduction algorithm which can be used in SLAM framework
resulting in information based reduced landmark SLAM. The
results of reduced landmark based SLAM algorithm are shown
on Victoria park dataset and a Synthetic dataset and are
compared with standard graph SLAM (SAM [6]) algorithm.
We demonstrate a reduction of 40-50% in the number of
landmarks and around 55% in the number of poses with
minimal estimation error as compared to standard SLAM
algorithm.
(Georgia Institute of Technology, 2010-12)
Rogers, John G.; Trevor, Alexander J. B.; Nieto-Granda, Carlos; Cunningham, Alexander; Paluri, Manohar; Michael, Nathan; Dellaert, Frank; Christensen, Henrik I.; Kumar, Vijay
This paper will explore the relationship between sensory accuracy and Simultaneous Localization and Mapping (SLAM) performance. As inexpensive robots are developed with commodity components, the relationship between performance level and accuracy will need to be determined. Experiments are presented in this paper which compare various aspects of sensor performance such as maximum range, noise, angular precision, and viewable angle. In addition, mapping results from three popular laser scanners (Hokuyo’s URG and UTM30, as well as SICK’s LMS291) are compared.