This work presents a method for automated generation of navigation plan for autonomous or robotic surface vessel. Historical Automatic Identification System (AIS) data is of significant value to this problem. The method joins AIS locations of a same vessel at different time and locations in a region into a route. Next, it automatically computes navigation plans using nearest neighbour based path retrieval relying on two representations, Ship Feature and Navigation Feature. Before starting service, existing AIS records in the form of ship properties and corresponding route are preprocessed and stored in the form of Ship and Navigation Feature. During online retrieval, given input constraints in vector form, nearest neighbour of this query vector in the same space is found and corresponding path of the neighbour is returned as recommended path. Analysis was done in four and two dimensional spaces for Ship and Navigation Feature respectively. Application of the method is demonstrated in two regions of Australian, covering Bass Strait and Great Australian Bight.

An autonomous framework to evaluate layout of a piping design in the form of piping and instrumentation diagram (P&ID) according to a set of standards of marine and offshore industry is proposed. The method starts with transforming a P&ID into a vector x in Rd. Transformation is done based on a concept introduced for piping known as Histogram of Connectivity. The proposed descriptor captures two essential properties of P&ID: attributes of each component and connectivity among the components. Next, linear support vector machine (SVM) is used to learn a classifier from existing compliant and noncompliant designs. Subsequently, the linear classifier can be used to check if an unseen design complies with the standards. In addition, to enable follow up on noncompliant design including correction or modification, a method to analyze the reason of noncompliance prediction by the learned SVM model is introduced. The method has demonstrated encouraging performance in two challenging data sets of designs created with advice from experienced engineers in the industry, based on International Convention for the Prevention of Pollution from Ships (MARPOL) and Rules for Classification of Ships of Lloyd's Register.

A framework is presented for estimating the pose of a camera based on images extracted from a single omnidirectional image of an urban scene, given a 2D map with building outlines with no 3D geometric information nor appearance data. The framework attempts to identify vertical corner edges of buildings in the query image, which we term VCLH, as well as the neighboring plane normals, through vanishing point analysis. A bottom-up process further groups VCLH into elemental planes and subsequently into 3D structural fragments modulo a similarity transformation. A geometric hashing lookup allows us to rapidly establish multiple candidate correspondences between the structural fragments and the 2D map building contours. A voting-based camera pose estimation method is then employed to recover the correspondences admitting a camera pose solution with high consensus. In a dataset that is even challenging for humans, the system returned a top-30 ranking for correct matches out of 3600 camera pose hypotheses (0.83% selectivity) for 50.9% of queries.