Sparse Bayesian information filters for localization and mapping
Citable URI
https://hdl.handle.net/1912/2585DOI
10.1575/1912/2585Abstract
This thesis formulates an estimation framework for Simultaneous Localization and
Mapping (SLAM) that addresses the problem of scalability in large environments.
We describe an estimation-theoretic algorithm that achieves significant gains in computational
efficiency while maintaining consistent estimates for the vehicle pose and
the map of the environment.
We specifically address the feature-based SLAM problem in which the robot represents
the environment as a collection of landmarks. The thesis takes a Bayesian
approach whereby we maintain a joint posterior over the vehicle pose and feature
states, conditioned upon measurement data. We model the distribution as Gaussian
and parametrize the posterior in the canonical form, in terms of the information
(inverse covariance) matrix. When sparse, this representation is amenable to computationally
efficient Bayesian SLAM filtering. However, while a large majority of the
elements within the normalized information matrix are very small in magnitude, it is
fully populated nonetheless. Recent feature-based SLAM filters achieve the scalability
benefits of a sparse parametrization by explicitly pruning these weak links in an effort
to enforce sparsity. We analyze one such algorithm, the Sparse Extended Information
Filter (SEIF), which has laid much of the groundwork concerning the computational
benefits of the sparse canonical form. The thesis performs a detailed analysis of the
process by which the SEIF approximates the sparsity of the information matrix and
reveals key insights into the consequences of different sparsification strategies. We
demonstrate that the SEIF yields a sparse approximation to the posterior that is inconsistent,
suffering from exaggerated confidence estimates. This overconfidence has
detrimental effects on important aspects of the SLAM process and affects the higher
level goal of producing accurate maps for subsequent localization and path planning.
This thesis proposes an alternative scalable filter that maintains sparsity while
preserving the consistency of the distribution. We leverage insights into the natural
structure of the feature-based canonical parametrization and derive a method that
actively maintains an exactly sparse posterior. Our algorithm exploits the structure
of the parametrization to achieve gains in efficiency, with a computational cost that
scales linearly with the size of the map. Unlike similar techniques that sacrifice
consistency for improved scalability, our algorithm performs inference over a posterior
that is conservative relative to the nominal Gaussian distribution. Consequently, we
preserve the consistency of the pose and map estimates and avoid the effects of an
overconfident posterior.
We demonstrate our filter alongside the SEIF and the standard EKF both in simulation
as well as on two real-world datasets. While we maintain the computational
advantages of an exactly sparse representation, the results show convincingly that
our method yields conservative estimates for the robot pose and map that are nearly
identical to those of the original Gaussian distribution as produced by the EKF, but
at much less computational expense.
The thesis concludes with an extension of our SLAM filter to a complex underwater
environment. We describe a systems-level framework for localization and mapping
relative to a ship hull with an Autonomous Underwater Vehicle (AUV) equipped
with a forward-looking sonar. The approach utilizes our filter to fuse measurements
of vehicle attitude and motion from onboard sensors with data from sonar images of
the hull. We employ the system to perform three-dimensional, 6-DOF SLAM on a
ship hull.
Description
Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2008
Suggested Citation
Thesis: Walter, Matthew R., "Sparse Bayesian information filters for localization and mapping", 2008-02, DOI:10.1575/1912/2585, https://hdl.handle.net/1912/2585Related items
Showing items related by title, author, creator and subject.
-
Internal hydraulic jumps with upstream shear
Ogden, Kelly A. (Massachusetts Institute of Technology and Woods Hole Oceanographic Institution, 2017-02)Internal hydraulic jumps in flows with upstream shear are investigated numerically and theoretically. The role of upstream shear has not previously been thoroughly investigated, although it is important in many oceanographic ... -
Insight into chemical, biological, and physical processes in coastal waters from dissolved oxygen and inert gas tracers
Manning, Cara C. (Massachusetts Institute of Technology and Woods Hole Oceanographic Institution, 2017-02)In this thesis, I use coastal measurements of dissolved O2 and inert gases to provide insight into the chemical, biological, and physical processes that impact the oceanic cycles of carbon and dissolved gases. Dissolved ... -
Coral biomineralization, climate proxies and the sensitivity of coral reefs to CO2-driven climate change
DeCarlo, Thomas M. (Massachusetts Institute of Technology and Woods Hole Oceanographic Institution, 2017-02)Scleractinian corals extract calcium (Ca2+) and carbonate (CO2−3) ions from seawater to construct their calcium carbonate (CaCO3) skeletons. Key to the coral biomineralization process is the active elevation of the CO2−3 ...