MeshSLAM: Robust Localization and Large-Scale Mapping in Barren Terrain

Award Information
Agency:
National Aeronautics and Space Administration
Branch
n/a
Amount:
$124,938.00
Award Year:
2013
Program:
STTR
Phase:
Phase I
Contract:
NNX13CA47P
Award Id:
n/a
Agency Tracking Number:
120080
Solicitation Year:
2012
Solicitation Topic Code:
T4.01
Solicitation Number:
n/a
Small Business Information
142 Crescent Drive, Pittsburgh, PA, 15228-1050
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
078669831
Principal Investigator:
David Wettergreen
Research Professor
(412) 268-5421
dsw@ri.cmu.edu
Business Contact:
Michael Wagner
Business Official
(412) 606-3842
mwagner@cmu.edu
Research Institution:
Carnegie Mellon University
Kristen Jackson
5000 Forbes Avenue
Pittsburgh, PA, 15213-15213
() -
Domestic nonprofit research organization
Abstract
Robots need to know their location to map of their surroundings but without global positioning data they need a map to identify their surroundings and estimate their location. Simultaneous localization and mapping (SLAM) solves these dual problems at once. SLAM does not depend on any kind of infrastructure and is thus a promising localization technology for NASA planetary missions and for many terrestrial applications as well.However, state-of-the-art SLAM depends on easily-recognizable landmarks in the robot's environment, which are lacking in barren planetary surfaces. Our work will develop a technology we call MeshSLAM, which constructs robust landmarks from associations of weak features extracted from terrain. Our test results will also show that MeshSLAM applies to all environments in which NASA's rovers could someday operate: dunes, rocky plains, overhangs, cliff faces, and underground structures such as lava tubes.Another limitation of SLAM for planetary missions is its significant data-association problems. As a robot travels it must infer its motion from the sensor data it collects, which invariably suffers from drift due to random error. To correct drift, SLAM recognize when the robot has returned to a previously-visited place, which requires searching over a great deal of previously-sensed data. Computation on such a large amount of memory may be infeasible on space-relevant hardware. MeshSLAM eases these requirements. It employs topology-based map segmentation, which limits the scope of a search. Furthermore, a faster, multi-resolution search is performed over the topological graph of observations.Mesh Robotics LLC and Carnegie Mellon University have formed a partnership to commercially develop MeshSLAM. MeshSLAM technology will be available via open source, to ease its adoption by NASA. In Phase 1 of our project we will show the feasibility of MeshSLAM for NASA and commercial applications through a series of focused technical demonstrations.

* information listed above is at the time of submission.

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