Simultaneous Localization and Mapping in Marine Environments

Accurate navigation is a fundamental requirement for robotic systems— marine and terrestrial. For an intelligent autonomous system to interact effectively and safely with its environment, it needs to accurately perceive its surroundings. While traditional dead-reckoning filtering can achieve extremely high performance, the localization accuracy decays monotonically with distance traveled. Other approaches (such as external beacons) can help; nonetheless, the typical prerogative is to remain at a safe distance and to avoid engaging with the environment. In this chapter we discuss alternative approaches which utilize onboard sensors so that the robot can estimate the location of sensed objects and use these observations to improve its own navigation as well its perception of the environment. This approach allows for meaningful interaction and autonomy. Three motivating autonomous underwater vehicle (AUV) applications are outlined herein. The first fuses external range sensing with relative sonar measurements. The second application localizes relative to a prior map so as to revisit a specific feature, while the third builds an accurate model of an underwater structure which is consistent and complete. In particular we demonstrate that each approach can be abstracted to a core problem of incremental estimation within a sparse graph of the AUV’s trajectory and the locations of features of interest which can be updated and optimized in real time on board the AUV. Maurice F. Fallon · Hordur Johannsson ·Michael Kaess · John Folkesson · Hunter McClelland · Brendan J. Englot · Franz S. Hover · John J. Leonard Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA. e-mail: mfallon@mit.edu,kaess@mit.edu,hordurj@mit.edu,johnfolk@mit.edu,huntermc@mit. edu,benglot@mit.edu,hover@mit.edu,jleonard@mit.edu Mae L. Seto et al., Marine Robot Autonomy, DOI 10.1007/978-1-4614-5658-2 6, © Springer Science+Business Media New York 2013 243