In the context of vehicle operations navigation and tracking are two of the most commonly used capabilities. Navigation is used for locating and pathfinding for individual agents, including people, vehicles, robots, and ships, as well as for groups or swarms of agents. Depending on the surrounding environment, navigation may be done with the help of external aids and measurements, or if operating in a novel or non-cooperative environment, my require the use only on-board sensing and decision making. Groups or swarms of agents can also work together to locate themselves in an environment or can coordinate to complete a series of tasks. While navigation is more concerned with self-awareness, tracking is more focused on identifying, labeling, and following other stationary or moving targets.
In tracking an object, feature, or agent the three different levels of fidelity at which a target may be perceived are: detection, classification, and identification. Detection is the lowest fidelity perception, which essentially states that “something of potential interest” has been observed. Classification takes perception one step further and labels what kind of target has been detected. Identification is the final step that offers the most actionable intelligence, identifying unique characteristics of a perceived target and even being able to differentiate between multiple simultaneously observed targets. Tracking also requires that perception of a target, or targets, be performed over some duration of time.
In performing either navigation or tracking, it is helpful to have a dynamic model which describes the movement of the bodies or features being studied. When performing navigation of, for example, a vehicle, it is useful to understand how your vehicle moves and behaves, so that you can propagate your current state (position, velocity, orientation, and other relevant parameters) into the future. Being able to predict how you are going to move in the near-term future is incredibly valuable in navigation problems and for robust navigation requires the use of state estimation algorithms and filters. These same methods can be used for tracking targets of interest as well. Once a target has been perceived, having an idea of how it’s moving can help maintain tracking, especially in crowded or cluttered environments.
Below are some of the capabilities we’ve developed while working on problems in the domain of Navigation and Tracking: