Passive underwater odometry using a video camera

• US 10,227,119 B2
• Filed: 04/17/2017
• Issued: 03/12/2019
• Est. Priority Date: 04/15/2016
• Status: Active Grant

• Current Assignee: Lockheed Martin Corporation (Martin Marietta Corporation)
• Original Assignee: Lockheed Martin Corporation (Martin Marietta Corporation)
• Inventors: Sadjadi, Firooz A., Tangirala, Sekhar C.
• Primary Examiner(s): Grant, II, Jerome
• Application Number:
  US15/489,016
• Publication Number:
  US 20170301098A1
• Time in Patent Office: 694 Days
• CPC Class Codes: B63G 2008/002 unmanned
• CPC Class Codes: B63G 8/38 Arrangement of visual or electronic watch equipment, e.g. of periscopes, of radar periscopes, optical aiming or sighting devices per se G02B23/00
• CPC Class Codes: G01P 3/38 using photographic means
• CPC Class Codes: G06T 2200/32 involving image mosaicing
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/30252 Vehicle exterior; Vicinity of vehicle
• CPC Class Codes: G06T 7/246 using feature-based methods, e.g. the tracking of corners or segments

Abstract

Systems and methods are described where odometry information that is obtained from a video camera mounted on an underwater vehicle is used to estimate the velocity of the underwater vehicle. The techniques described herein estimate the velocity of the underwater vehicle passively without emitting sound or other energy from the underwater vehicle.

Claims

1. A method of real-time estimation of velocity of an underwater vehicle, comprising:

as the underwater vehicle is traveling underwater, capturing two-dimensional video of an underwater structure using a non-stereo video camera mounted on the underwater vehicle;

selecting first and second two-dimensional image frames from the two-dimensional video, the first and second two-dimensional image frames are captured at different times;

enhancing the first and second two-dimensional image frames to produce enhanced first and second two-dimensional image frames, wherein enhancing the first and second two-dimensional image frames comprises generating an empirical probability density function or model of the water the underwater vehicle is traveling in, and using the empirical probability density function or model to modify the first and second two-dimensional image frames in situ to match an expected probability density function or model; and

estimating the velocity of the underwater vehicle using the enhanced first and second two-dimensional image frames.

2. The method of claim 1, wherein the underwater vehicle is an unmanned underwater vehicle, and further comprising outputting the estimated velocity to a navigation system of the unmanned underwater vehicle.

3. The method of claim 1, where the underwater structure is a sea floor.

4. The method of claim 1, further comprising performing image transformation and mosaicking of the enhanced first and second two-dimensional image frames.

5. The method of claim 1, wherein estimating the velocity of the underwater vehicle comprises generating a transformation matrix for each of the enhanced first and second two-dimensional image frames;

and deriving velocity components of the underwater vehicle from the transformation matrix for each of the enhanced first and second two-dimensional image frames, information about the distance of the underwater vehicle from the underwater structure, and resolution of the non-stereo video camera.

6. A method of real-time estimation of velocity of a vehicle, comprising:

as the vehicle is traveling past a structure, capturing two-dimensional video of the structure using a non-stereo video camera mounted on the vehicle;

selecting first and second two-dimensional image frames from the two-dimensional video, the first and second two-dimensional image frames are captured at different times;

enhancing the first and second two-dimensional image frames to produce enhanced first and second two-dimensional image frames, wherein enhancing the first and second two-dimensional image frames comprises generating an empirical probability density function or model of the medium the vehicle is traveling in, and using the empirical probability density function or model to modify the first and second two-dimensional image frames in situ to match an expected probability density function or model; and

estimating the velocity of the vehicle using the enhanced first and second two dimensional image frames.