|
|
|
| RI | Thesis Oral | 16 Jul 2002 | |
Robotics Institute Thesis Oral 16 Jul 2002
Place and Time |
Abstract |
Further Details |
Thesis Committee
Bearings Only Localization and Mapping
Matthew Deans
Robotics Institute
Carnegie Mellon University
| Place and Time |
NSH 3305
5:00 PM
| Abstract |
In many applications, mobile robots must be able to localize themselves
with respect to environments which are not known a priori in order to
navigate and accomplish tasks. This means that the robot must be able to
build a map of an unknown environment while simultaneously localizing
itself within that map. The so called Simultaneous Localization and
Mapping or SLAM problem is a formulation of this requirement, and has been the subject of a considerable amount of robotics research in the last
decade.
This thesis looks at the problem of localization and mapping when the only
information available to the robot is measurements of relative motion and
bearings to features. The relative motion sensor measures displacement
from one time to the next through some means such as inertial measurement or odometry, as opposed to externally referenced position measurements like compass or GPS. The bearing sensor measures the direction toward features from the robot through a sensor such as an omnidirectional camera, as opposed to bearing and range sensors such as laser rangefinders, sonar, or millimeter wave radar.
A full solution to the bearing-only SLAM problem must take into
consideration detecting and identifying features and estimating the
location of the features as well as the motion of the robot using the
measurements. This thesis focuses on the estimation problem given that
feature detection and data association are available. Estimation requires
a solution that is fast, accurate, consistent, and robust.
This dissertation puts forth a methodology for building maps and localizing
a mobile robot using odometry and monocular vision. This sensor suite is
chosen for its simplicity and generality, and in some sense represents a
minimal configuration for localization and mapping. In a broader sense,
this dissertation describes a novel method for state estimation applicable
to problems which exhibit particular nonlinearity and sparseness
properties. The method relies on deterministic sampling in order to
compute posterior statistics at each time step in a recursive filter. The
relationship of the new algorithm to bundle adjustment and Kalman filtering
(including some of its variants) is discussed.
| Further Details |
A copy of the thesis oral document can be found at http://www.cs.cmu.edu/~deano/research/documents/thesis-draft.ps.gz.
| Thesis Committee |
This page maintained by robotwebmaster@ri.cmu.edu.