Teach-and-repeat path following for an autonomous underwater vehicle

This paper presents a teach-and-repeat path-following method for an autonomous underwater vehicle (AUV) navigating long distances in environments where external navigation aides are denied. This method utilizes sonar images to construct a series of reference views along a path,stored as a topological map. The AUV can then renavigate along this path, either to return to the start location or to repeat the route. Utilizing unique assumptions about the sonar image-generation process, this system exhibits robust image-matching capabilities, providing observations to a discrete Bayesian filter that maintains an estimate of progress along the path. Image-matching also provides an estimate of offset from the path, allowing the AUV to correct its heading and effectively close the gap. Over a series of field trials, this system demonstrated online control of an AUV in the ocean environment of Holyrood Arm, Newfoundland and Labrador, Canada. The system was implemented on an International Submarine Engineering Ltd. Explorer AUV and per-formed multiple path completions over both a 1 and 5 km track. These trials illustrated an AUV operating in a fully autonomous mode, in which navigation was driven solely by sensor feedback and adaptive control. Path-following performance was as desired, with the AUV maintaining close offset to the path

Sonar Image Registration for Localization of an Underwater Vehicle

This paper presents a system to provide augmented localization to an AUV equipped with a side scan sonar. Upon revisiting an area, from which side scan data had previously been collected, the system generates an estimate to bound the error in the AUV’s estimate. Localization is accomplished through the comparison of sonar images. Image comparison is based on the extraction of features which characterize local gradient distributions, such as Lowe’s SIFT feature extractor. To resolve potential ambiguities and noise in the image comparison measurement, the localization system incorporates a Bayesian inference algorithm that considers both image based measurement and relative motion to refine the position estimate over time. We describe the particular methods, constraints and augmentations used to apply established image matching and alignment techniques to side scan sonar imagery. By applying consistent geographical corrections to the raw sonar data; using a flat-bottom assumption; and by adding the constraint that images are formed with north aligned up; the traditional problem of full pose estimation is reduced to the two-dimensional case of determining only the x,y translation independent of vehicle altitude. Due to the assumption of constant scale and orientation between images, sensitivity of image feature matching is shown to be controllable by filtering feature matches based on comparing their scale and orientation. This effect was quantified using binary classification analysis. The system’s performance was measured by performing tests on a large side scan survey which represents the familiar terrain that a returning AUV could use for localization

Towards AUV Route Following Using Qualitative Navigation

Abstract-We present a novel approach to the guidance of an autonomous underwater vehicle (AUV) along a trained route. The introduced system employs a topological route representation based on storing a sequence of side-scan sonar images captured along the route. When in following mode, image registration techniques provide the vehicle with a realtime estimate of the direction of its displacement relative to the trained route. This simplified approach to navigation sidesteps the problems inherent with maintaining a vehicle pose estimate within a global reference system, thereby allowing the vehicle to traverse a trained route without resorting to external navigation aides (e.g. GPS). Simulation results are provided which validate the proof of concept for our approach