William’s paper discusses an approach to autonomous navigation for an undersea vehicle that uses information from a scanning sonar to generate navigation estimates based on a simultaneous localization and mapping algorithm. The key elements of the research include the development of sonar feature models, tracking and use of these models in mapping and position estimation, and the development of low-speed platform models for vehicle control. It is interesting that in underwater expeditions that regular maps are seldom available and so other methods for localization are needed. The key is that the underwater vehicle relies on its ability to extract useful navigation information from the data returned by its sensors.

In order to accomplish the reading of the surroundings from relative observations of landmarks, it simultaneously computes an estimate of vehicle location and an estimate of landmark locations. While continuing in motion, the robot builds a complete map of landmarks and uses these to provide continuous estimates of the vehicles location. This approach is in line with the major goal of this research, which is to provide autonomous navigation using the information provided by the vehicle’s on-board sensors. This is an interesting solution to a potentially complex problem because of the difficulties of understanding the surroundings in an underwater environment. It is a powerful feature to be able to extract information from the surroundings in order to dynamically localize yourself.

The localization and map building process consists of a recursive, three-stage procedure comprising prediction, observation and update steps using an extended Kalman filter. Utilizing this algorithm, the system is able to identify point features from the sonar scans returned by the imaging sonar and are used to build up a map of the environment. The ability to use natural features will allow the submersible to be deployed in a larger range of environments without the need to introduce artificial beacons. By using terrain information as a navigational aid, the vehicle is able to detect unmodeled disturbances in its motion induced by the current.

In conclusion, we can see how Kalman filters are utilized to solve a complex problem of underwater navigation. Localizing to the features in the underwater area is a great method for underwater vessels. It would be nice if this feature could be integrated into some waypoint system so that the captain of the mission could plot out a course for the submersible. This would allow the submersible to dynamically adapt to the underwater situation and follow a course outlined previously. This would also force the planner to consider the sensor strategies for the underwater environment.

Reference:

  1. Williams, Stefan B. “Towards terrain-aided navigation for underwater robotics.” <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.69.3561&rep=rep1&type=pdf>