Introduction

The inquisitive nature of humans has lead to the comprehensive exploration and mapping of land masses on planet earth, subsequently scientists are now turning to the oceans to discover new possibilities for telecommunications, biological & geological resources and energy sources. Underwater vehicles play an important role in this exploration as the deep ocean is a harsh and unforgiving environment for human discovery. Unmanned underwater vehicles (UUV) are utilised for many different scientific, military and commercial applications such as high resolution seabed surveying (Yoerger et al. 2000), mine countermeasures (Freitag et al. 2005), inspection and repair of underwater man-made structures (Kondo & Ura 2004) and wreck discovery and localisation (Eustice et al. 2005). Accurate vehicle position knowledge is vital for all underwater missions for correct registration between sensor and navigation data and also for control and final recovery of the vehicle. The characteristics of the underwater environment pose a plethora of difficult challenges for vehicle navigation and these obstacles differ greatly from the issues encountered in land, air and space based navigation (Whitcomb 2000). The rapid attenuation of acoustic and electromagnetic radiation in water restricts the range of acoustic and optical sensors and also limits communication bandwidth. As a consequence of this severe absorption acoustic and optical sensors require submersion near to the survey mission site to gather accurate high resolution data sets. The limitation on communication bandwidth means that vehicle autonomy can only be achieved when the large majority of computation is performed onboard. Whereas land based vehicles can rely on Global Positioning System (GPS) for accurate 3D position updates, the underwater equivalent acoustic transponder network is limited by range, accuracy, the associated cost and deployment & calibration time.

Another challenge that is faced with underwater navigation is the intrinsic ambient pressure. While terrain based vehicle developers have to consider the relatively simplistic and well understood nature of atmospheric pressure in sensor and actuator design, underwater pressure, increasing at a rate of approximately 1 atmosphere (14.7 psi) every 10 meters of depth, can greatly influence and restrict sensor and actuator design. Other issues such as the inherent presence of waves and underwater currents can make the task of accurately describing vehicle motion more difficult and, as a result, affect the accuracy of vehicle navigation.

Many of these problems cannot be overcome directly so the underwater community relies on improving the navigation sensors and the techniques in which the sensor data is interpreted. The development of more advanced navigation sensors is motivated by the need to expand the capabilities and applicability of underwater vehicles and to increase the accuracy, quantity and cost effectiveness of oceanographic data collection. Sensor selection can depend on many factors including resolution, update rate, cost, calibration time, depth rating, range, power requirements and mission objectives. In general the accuracy of a particular sensor is directly proportional to its expense. This has lead to increased research efforts to develop more precise lower cost sensors and improve data interpretation by implementing more intelligent computation techniques such as multi sensor data fusion (MSDF). Many commercially available underwater positioning sensors exist but unfortunately no one sensor yet provides the perfect solution to all underwater navigation needs so, in general, combinations of sensors are employed. The current state of the art navigation systems are based on the use of velocity measurements from a Doppler velocity log (DVL) sensor conveniently fused with accurate velocity/angular rate and position/attitude measurements derived by integration and double integration respectively of linear acceleration and angular rates from an inertia! measurement unit (IMU) (Kinsey et al. 2006). To bound the inherent integration drift in the system position fixes from an acoustic transponder network such as Long Baseline (LBL), Ultra Short Baseline (USBL) or GPS Intelligent Buoys (GIB) are commonly used. However, this option raises the mission cost as transponders require deployment prior to the mission or a mother ship is necessary. This solution also limits the area in which the vehicle can accurately navigate to within the bounds of the transponder network (acoustic tether).

Over recent years, computer vision has been the subject of increased interest as a result of improving hardware processing capabilities and the need for more flexible, lightweight and accurate sensor solutions (Horgan & Toal 2006). Many researchers have explored the possibility of using computer vision as a primary source for UUV navigation. Techniques for implementing computer vision in order to track cables on the seabed for inspection and maintenance purposes have been researched (Balasuriya & Ura 2002; Ortiz et al. 2002). Station keeping, the process of maintaining a vehicle's pose, is another application that has taken advantage of vision system's inherent accuracy and high update rates (Negahdaripour et al. 1999; van der Zwaan et al. 2002). Motion estimation from vision is of particular interest for the development of intervention class vehicle navigation (Caccia 2006). Wreckage visualization and biological and geological surveying are examples of applications that use image mosaicking techniques to acquire a human interpretable view of the ocean floor but it has also been proven as an appropriate means for near seabed vehicle navigation (Negahdaripour & Xu 2002; Garcia et al. 2006).

This chapter gives and introduction to the field of vision based unmanned underwater vehicle navigation and details the advantages and disadvantages of such systems. A review of recent research efforts in the field of vision based UUV navigation is also presented. This review is discussed under the following headings in relation to recent literature reviewed: image mosaicking, cable tracking, station keeping and positioning & localisation. This chapter also considers the applications of sensor fusion techniques for underwater navigation and these are also considered with reference to recent literature. The author gives an opinion about the future of each application based on the presented review. Finally conclusions of the review are given.

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