(Georgia Institute of Technology, 2008-05)
Mottaghi, Roozbeh; Kaess, Michael; Ranganathan, Ananth; Roberts, Richard; Dellaert, Frank
Pose estimation of outdoor robots presents some
distinct challenges due to the various uncertainties in the robot
sensing and action. In particular, global positioning sensors of
outdoor robots do not always work perfectly, causing large drift
in the location estimate of the robot. To overcome this common
problem, we propose a new approach for global localization
using place recognition. First, we learn the location of some
arbitrary key places using odometry measurements and GPS
measurements only at the start and the end of the robot
trajectory. In subsequent runs, when the robot perceives a key
place, our fixed-lag smoother fuses odometry measurements
with the relative location to the key place to improve its pose
estimate. Outdoor mobile robot experiments show that place
recognition measurements significantly improve the estimate of
the smoother in the absence of GPS measurements.
(Georgia Institute of Technology, 2005-04)
Kaess, Michael; Dellaert, Frank
The problem of simultaneous localization
and mapping has received much attention over the last
years. Especially large scale environments, where the robot
trajectory loops back on itself, are a challenge. In this
paper we introduce a new solution to this problem of
closing the loop. Our algorithm is EM-based, but differs
from previous work. The key is a probability distribution
over partitions of feature tracks that is determined in
the E-step, based on the current estimate of the motion.
This virtual structure is then used in the M-step to
obtain a better estimate for the motion. We demonstrate
the success of our algorithm in experiments on real laser data.