(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, 2008)
Kaess, Michael; Ranganathan, Ananth; Dellaert, Frank
We present incremental smoothing and mapping
(iSAM), a novel approach to the simultaneous localization and
mapping problem that is based on fast incremental matrix
factorization. iSAM provides an efficient and exact solution by
updating a QR factorization of the naturally sparse smoothing information
matrix, therefore recalculating only the matrix entries
that actually change. iSAM is efficient even for robot trajectories
with many loops as it avoids unnecessary fill-in in the factor
matrix by periodic variable reordering. Also, to enable data
association in real-time, we provide efficient algorithms to access
the estimation uncertainties of interest based on the factored
information matrix. We systematically evaluate the different
components of iSAM as well as the overall algorithm using
various simulated and real-world datasets for both landmark
and pose-only settings.