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

Award Information

Agency:
National Aeronautics and Space Administration
Branch:
N/A
Award ID:
Program Year/Program:
2013 / STTR
Agency Tracking Number:
120080
Solicitation Year:
2012
Solicitation Topic Code:
T4.01
Solicitation Number:
Small Business Information
Mesh Robotics, LLC
142 Crescent Drive Pittsburgh, PA 15228-1050
View profile »
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No
 
Phase 1
Fiscal Year: 2013
Title: MeshSLAM: Robust Localization and Large-Scale Mapping in Barren Terrain
Agency: NASA
Contract: NNX13CA47P
Award Amount: $124,938.00
 

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.

Principal Investigator:

David Wettergreen
Research Professor
4122685421
dsw@ri.cmu.edu

Business Contact:

Michael Wagner
Business Official
4126063842
mwagner@cmu.edu
Small Business Information at Submission:

Mesh Robotics, LLC
142 Crescent Drive Pittsburgh, PA 15228-1050

EIN/Tax ID: 461306680
DUNS: N/A
Number of Employees:
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No
Research Institution Information:
Carnegie Mellon University
5000 Forbes Avenue
Pittsburgh, PA 15213-15213
Contact: Kristen Jackson