Simultaneous Localization and Mapping for Planetary Surface Mobility

Award Information
National Aeronautics and Space Administration
Award Year:
Phase I
Agency Tracking Number:
Solicitation Year:
Solicitation Topic Code:
Solicitation Number:
Small Business Information
ProtoInnovations, LLC
1908 Shaw Avenue, Pittsburgh, PA, 15217-1710
Hubzone Owned:
Minority Owned:
Woman Owned:
Principal Investigator:
David Wettergreen
Principal Investigator
(412) 268-5421
Business Contact:
Dimi Apostolopoulos
Business Official
(412) 916-8807
Research Institution:
Carnegie Mellon University
Kristen Rotolo
5000 Forbes Ave.
Pittsburgh, PA, 15213-3890
() -
Nonprofit college or university
ProtoInnovations, LLC and Carnegie Mellon University have formed a partnership to commercially develop localization and mapping technologies for planetary rovers. Our first aim is to provide a reliable means of localization that is independent of infrastructure, such as GPS, and compatible with requirements of missions to planetary surfaces. Simultaneously solving for the precise location of the rover as it moves while building an accurate map of the environment is an optimization problem involving internal sensing, sensing of the surrounding environment, probabilistic optimization methods, efficient data structures, and a robust implementation. Our second aim is to merge simultaneous localization and mapping (SLAM) technologies with our existing Reliable Autonomous Surface Mobility (RASM) architecture for rover navigation. Our unique partnership brings together state-of-the-art technologies for SLAM with experience in delivering and supporting both autonomous systems and mobility platforms for NASA.Our proposed project will create a SLAM framework that is capable of accurately localizing a rover throughout long, multi-kilometer traverses of barren terrain. Our approach is compatible with limited communication and computing resources expected for missions to planetary surfaces. Our technologyis based on innovative representations of evidence grids, particle-filter algorithms that operate on range data rather than explicit features, and strategies for segmenting large evidence grids into manageable pieces. In this project we will evaluate the maturity of these algorithms, developed for research programs at Carnegie Mellon, and incorporate them into our RASM architecture, thus providing portable and reliable localization for a variety of vehicle platforms and sensors. Mission constraints will vary broadly, so our SLAM components will be able to merge readings from various suitesof sensors that may be found on planetary rovers.

* information listed above is at the time of submission.

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