Innovative Simultaneous Localization and Mapping Techniques for Unmanned Underwater Vehicles

Navy SBIR 21.1 - Topic N211-036
NAVSEA - Naval Sea Systems Command - Mr. Dean Putnam - dean.r.putnam@navy.mil
Opens: January 14, 2021 - Closes: February 18, 2021 (12:00pm EDT)

N211-036 TITLE: Innovative Simultaneous Localization and Mapping Techniques for Unmanned Underwater Vehicles

RT&L FOCUS AREA(S): Autonomy

TECHNOLOGY AREA(S): Ground / Sea Vehicles

The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with section 3.5 of the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws.

OBJECTIVE: The development of robust Simultaneous Localization and Mapping (SLAM) techniques for assisting the navigation of Unmanned Underwater Vehicles operating in GPS-denied environments.

DESCRIPTION: Despite the considerable effort directed towards UUV navigation, a self-contained solution remains a key challenge. Due to the cumulative error that an inertial navigation system (INS) experiences with time, UUVs typically require regular surfacing to obtain GPS fixes, or the presence of acoustic localization beacons, in order to correct position drift. Such options can be undesirable/unavailable in certain applications (e.g., deep-water and/or Intelligence, Surveillance, and Reconnaissance (ISR) missions). Underwater Terrain Aided Navigation (TAN) methods have also demonstrated the ability to provide accurate navigation resets, though they are limited by the requirement for accurate high-resolution reference bathymetry maps, which are not available for much of the Earthís sea floor. In response to the presently limited navigation capability, this topic will focus on the development of robust SLAM algorithms to assist UUV navigation in GPS-denied environments.

The Navy is rapidly developing and fielding a family of Unmanned Undersea Vehicles (UUV) specifically designed for operations within GPS-denied environments. Advances in underwater sensing technology and computing power have yielded new possibilities in the underwater domain. For instance, advanced sensor processing and new underwater navigation techniques have become available, including SLAM. SLAM broadly refers to the problem of jointly creating (and updating) a map of an unknown environment and estimating the systemís position and pose within it. The topic has attracted a flurry of research in the robotics community over the past three decades, including indoor, land-based, aerial and even underwater vehicles. It has been a critical tool in the development of commercial robot vacuum systems, allowing them to operate in any home without prior knowledge of the layout. Other examples include the field of self-driving cars, where SLAM serves as a supplement to GPS navigation, allowing the system to build obstacle maps of the surrounding environment, and continue driving in unmapped areas or when GPS becomes unavailable.

Although SLAM has been proven effective for mobile robots operating in structured environments, the application of these techniques in the highly unstructured underwater domain presents unique challenges. As a result, there is still considerable room for growth in the use of SLAM techniques for UUVs. Some examples of SLAM-based approaches for UUVs include applications for achieving improved velocity-over-ground estimates, and algorithms for improving the accuracy of bathymetric maps generated from a UUV survey. For many UUV SLAM applications, the ultimate goal is to take advantage of the process to reduce position error growth, not necessarily to generate a map of the environment. Likewise, for this topic the UUV will not need to rely on mapping its entire operational environment in order to conduct the mission. As advances in energy technology continue to increase the endurance and operating range of UUVs, missions will cover wider areas, longer distances, and longer times. It can be assumed that the target UUV system for this effort will feature a navigation-grade INS as the baseline navigation system. PMS 406, Unmanned Maritime Systems program office, seeks the development of robust SLAM algorithms that will increase the mission capabilities of such UUVs by providing additional methods for aiding vehicle navigation. The goal is to increase overall navigational accuracy during a GPS-denied mission beyond what can be achieved with just the standard Doppler Velocity Logger (DVL) aiding to the INS, and provide a means of resetting what otherwise would be unbounded position error growth.

While initial validation of the algorithms can leverage off-line post-processing of vehicle and sensor data, the ultimate system design needs to provide output in-situ that can aid the UUV during the mission. Additionally, the solution should address the limitation of operating in areas without prior knowledge of the bathymetry or specific bottom features. Prior reference information, where available (i.e., any knowledge about natural or man-made features) can be used to enhance performance, however the system must also be capable of operating without any such assistance.

The algorithms developed should be utilized in a wide range of different environments and mission scenarios. This includes both rugged and smooth terrain, as well as cluttered and un-cluttered environments. A list of some potential Navy mission concepts and scenarios will be provided during Phase II. The system should be designed to serve as an aiding source for a UUV navigation framework based on a navigation grade INS. The solution should not be an integral piece of the UUV navigation system to the point that it needs to be operating continuously in order for the vehicle itself to navigate. Instead, the system solution encompassing the SLAM algorithms is expected to provide outputs that can be used as aiding sources into an INS framework.

The proposer will identify the available environmental information, features the algorithms aim to extract and the necessary sensors and sensor processing needed to utilize this information. The company will address how the algorithms are applicable to different UUV mission scenarios across a range of potential operational environments. The company will identify the vehicle behaviors and maneuvering necessary to utilize the algorithms and how these behaviors fit into the context of the overall vehicle mission. The concept will cover how the algorithms address areas where no prior information is available and the handling of both cluttered and un-cluttered environments. The company will identify the output data products of the algorithms and how this data aids the performance of the UUV navigation framework.

It is envisioned that the solution be tailored to aiding a UUV navigation framework based on a commercial off-the-shelf (COTS) Inertial Navigation System. Additionally, the solution may provide a means of saving new maps generated on-board or updating existing maps stored on the system for future use. The proposer will provide a detailed plan for validating the algorithms in a computer simulation environment. This test plan should include the types of vehicle and sensor data, both historical and simulated, that would be required to carry out relevant simulation test cases, and how such data will be acquired and/or generated. Phase II shall also include the development of a plan for at-sea tests of the computer program on government-owned UUVs and a list of validation metrics for such tests.

To ensure interoperability with PMS 406 portfolio, the solution must comply with the Unmanned Maritime Autonomy Architecture (UMAA). UMAA establishes a standard for common interfaces and software reuse among the mission autonomy and the various vehicle controllers, payloads, and Command and Control (C2) services in the PMS 406 portfolio of UxS vehicles. The UMAA common standard for Interface Control Documents (ICDs) mitigates the risk of vendor lock from proprietary autonomy solutions; effects cross-domain interoperability of UxS vehicles; and allows for open architecture (OA) modularity of autonomy solutions, control systems, C2, and payloads. The Navy will provide the open standards for UMAA upon award of Phase I.

Work produced in Phase II may become classified. Note: The prospective contractor(s) must be U.S. Owned and Operated with no Foreign Influence as defined by DOD 5220.22-M, National Industrial Security Program Operating Manual, unless acceptable mitigating procedures can and have been implemented and approved by the Defense Counterintelligence Security Agency (DCSA). The selected contractor and/or subcontractor must be able to acquire and maintain a secret level facility and Personnel Security Clearances, in order to perform on advanced phases of this contract as set forth by DCSA and NAVSEA in order to gain access to classified information pertaining to the national defense of the United States and its allies; this will be an inherent requirement. The selected company will be required to safeguard classified material IAW DoD 5220.22-M during the advance phases of this contract.

PHASE I: Phase I will consist of a concept and feasibility determination on the implementation of SLAM-based techniques for aiding a UUV navigation system during long-duration submerged missions. Feasibility determination will describe a path for development of SLAM algorithms that leverage existing navigation-grade INS solutions and current UUV payload sensor technology to assist in managing position error drift in accordance with the requirements within the Description section of this document.

The Phase I Option, if exercised, will include a detailed outline for a prototype system design for implementation in Phase II and a detailed plan for validating the algorithms in a computer simulation environment. This plan should include the types of vehicle and sensor data, both historical and simulated, that would be required to carry out relevant simulation test cases, and how such data will be acquired and/or generated. Additionally, the company will develop a comprehensive summary of how the proposed solution can address the challenge of improving state-of-the-art UUV navigation systems for long-range missions.

PHASE II: The Phase II effort will focus on implementing the SLAM algorithms proposed and outlined in Phase I by developing and delivering a prototype system. The simulation test plan outlined in Phase I should be used for initial validation and testing of this prototype system during development. Relevant vehicle navigation and sensor data feeds, generated through playback of historical datasets and/or simulation, will be used to create suitable test cases to demonstrate the feasibility of the proposed approach.

A successful Phase II project will demonstrate that the algorithms and prototype system can perform as expected using data representative of a variety of environments and deliver a detailed plan for the integration of the proposed algorithms into a software application compatible with government-owned UUV software architectures. This includes specifying a software interface compliant with the Unmanned Maritime Autonomy Architecture (UMAA). It is envisioned that the solution be tailored to aiding a UUV navigation framework based on a COTS Inertial Navigation System. Additionally, the solution may provide a means of saving new maps generated on-board or updating existing maps stored on the system for future use. Phase II shall also include the development of a plan for at-sea tests of the computer program on government-owned UUVs and a list of validation metrics for such tests.

It is probable that the work under this effort will be classified under Phase II (see Description section for details).

PHASE III DUAL USE APPLICATIONS: Assist the Navy in integrating the technology for Navy use. The proposed prototype will be integrated into the software architecture of Navy UUV systems. This includes both research-oriented UUV systems performing Science and Technology missions, as well as acquisition program UUVs conducting Navy missions at sea.

The proposed solution has applicability in a wide variety of commercial as well as defense applications. Organizations that require the use of UUVs for tasks such as inspecting and repairing submerged infrastructure, searching for airplane black-boxes, conducting port and harbor security and collecting environmental data or mapping the sea floor, can leverage this technology to increase navigational and mission reliability. There are significant advantages in transitioning this technology to other DoD agencies, government, and private sector entities to enhance UUV mission capability.

REFERENCES:

  1. Paull, Liam, et al. "UUV navigation and localization: A review." IEEE Journal of Oceanic Engineering 39.1 (2013): 131-149. https://www.semanticscholar.org/paper/AUV-Navigation-and-Localization%3A-A-Review-Paull-Saeedi/b141c78f429df09b532b8c996b321eae5983f27e
  2. Palomer, Albert, Pere Ridao, and David Ribas. "Multibeam 3D underwater SLAM with probabilistic registration." Sensors 16.4 (2016): 560. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4851074/
  3. Ribas, David, et al. "Underwater SLAM in a marina environment." 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2007. https://www.researchgate.net/publication/224296480_Underwater_SLAM_in_a_marina_environment
  4. P. Moutarlier and R. Chatila. An experimental system for incremental environment modeling by an autonomous mobile robot. In Proceedings of the 1st International Symposium on Experimental Robotics, Montreal, Canada, June 1989. https://link.springer.com/chapter/10.1007/BFb0042528
  5. R. Smith, M. Self, and P. Cheeseman. Estimating Uncertain Spatial Relationships in Robotics. Autonomous Robot Vehicles. Springer-Verlag, 1990. https://arxiv.org/abs/1304.3111

KEYWORDS: Unmanned Undersea Vehicles; UMAA; Navigation in GPS-denied environments; Undersea Mapping; SLAM; INS.

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