(Georgia Institute of Technology, 2012-07)
Kaess, Michael; Williams, Stephen; Indelman, Vadim; Roberts, Richard; Leonard, John J.; Dellaert, Frank
This paper presents a novel algorithm for integrating
real-time filtering of navigation data with full map/trajectory
smoothing. Unlike conventional mapping strategies, the result of
loop closures within the smoother serve to correct the real-time
navigation solution in addition to the map. This solution views
filtering and smoothing as different operations applied within a
single graphical model known as a Bayes tree. By maintaining all
information within a single graph, the optimal linear estimate is
guaranteed, while still allowing the filter and smoother to operate
asynchronously. This approach has been applied to simulated
aerial vehicle sensors consisting of a high-speed IMU and stereo
camera. Loop closures are extracted from the vision system in
an external process and incorporated into the smoother when
discovered. The performance of the proposed method is shown
to approach that of full batch optimization while maintaining
real-time operation.
(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.