Path Reconstruction From Nontraditional Sensor Information Using Subgraph Isomorphism Algorithms

Many mobile, exploratory machines are instrumented with sensors sensors, including radar, metal, pressure, and temperature, among others, to capture information about an environment. Often, the requirements of this type of data collection include the mapping and positioning of each of the data points but this can be difficult due to environment, operation, or equipment constraints. Traditional sensing sub-systems - such as accelerometers, Global Navigation Satellite Systems (GNSS), or camera-based vision - are commonly used to record location information. We propose a new tracking methodology that enables the reconstruction of the machine's path when these traditional positioning sensors are not present. We examine our proposed approach by applying it within hand-held hazardous object detection. In this work we examine the physical space modeling component of our approach. We show that the ground can be modeled as a set of sub-regions and use relational graphs to represent regions. Using simulated region information we show that the objects path can be solved for using isomorphism algorithms.