Abstract
This paper describes an approach to the construction of three-dimensional stochastic models for intelligent systems exploring an underwater environment. Important characteristics shared by such applications are: (1) real-time constraints; (2) unstructured, three-dimensional terrain; (3) high-bandwidth sensors providing redundant, overlapping coverage; (4) lack of prior knowledge about the environment; and (5) inherent inaccuracy or ambiguity in sensing and interpretation. The paper develops an underlying theory of stochastic backprojection and demonstrates how such an approach satisfies these five needs for undersea robotics.
Models are cast as three-dimensional spatial decompositions of stochastic feature vectors. A numerical approach to incorporating new sensor information is derived from an incremental adaptation of the summation method for image reconstruction. Error and ambiguity are accounted for by blurring a spatial projection of remote-sensor data before combining them stochastically with the model. By exploiting the redundancy in high-bandwidth sensing, model certainty and resolution are enhanced as more data accumulate. In the case of a three-dimensional profiling sonar, the model converges to a “fuzzy” surface distribution from which a deterministic surface map is extracted.
To verify the fundamental properties of stochastic backprojection and the resulting models, computer simulations are used to demonstrate: impulse and ramp responses; mitigation of artifacts caused by deterministic processing; incremental increase in accuracy and reduction of uncertainty; and convergence. Two examples illustrate how this approach has been successfully applied in the field for three-dimensional modeling of a sunken shipwreck by a remote undersea vehicle and for backscatter modeling of undersea terrain.
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Stewart, W.K. Three-dimensional stochastic modeling using sonar sensing for undersea robotics. Auton Robot 3, 121–143 (1996). https://doi.org/10.1007/BF00141151
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DOI: https://doi.org/10.1007/BF00141151