(Georgia Institute of Technology, 2010-01-29)
Kaess, Michael; Ila, Viorela; Roberts, Richard; Dellaert, Frank
In this paper we present a novel data structure, the Bayes tree, which exploits
the connections between graphical model inference and sparse linear algebra.
The proposed data structure provides a new perspective on an entire class of
simultaneous localization and mapping (SLAM) algorithms. Similar to a junction
tree, a Bayes tree encodes a factored probability density, but unlike the junction
tree it is directed and maps more naturally to the square root information matrix of
the SLAM problem. This makes it eminently suited to encode the sparse nature of
the problem, especially in a smoothing and mapping (SAM) context. The inherent
sparsity of SAM has already been exploited in the literature to produce efficient
solutions in both batch and online mapping. The graphical model perspective
allows us to develop a novel incremental algorithm that seamlessly incorporates
reordering and relinearization. This obviates the need for expensive periodic batch
operations from previous approaches, which negatively affect the performance
and detract from the intended online nature of the algorithm. The new method
is evaluated using simulated and real-world datasets in both landmark and pose
SLAM settings.
(Georgia Institute of Technology, 2006)
Kaess, Michael; Dellaert, Frank
Camera-based simultaneous localization and mapping or visual SLAM has received much attention recently. Typically single cameras, multiple cameras in a stereo setup or omni-directional cameras are used. We propose a different approach, where multiple cameras can be mounted on a robot in an arbitrary configuration. Allowing the cameras to face in different directions yields better constraints than single cameras or stereo setups can provide, simplifying the reconstruction of large-scale environments. And in contrast to omni-directional sensors, the available resolution can be focused on areas of interest depending on the application. We describe a sparse SLAM approach that is suitable for real-time reconstruction from such multi-camera configurations. We have implemented the system and show experimental results in a large-scale environment, using a custom made eight-camera rig.