This paper presents a method for determining the GPS location of a ground-based object when imaged from a fixed-wing miniature air vehicle (MAV). Using the pixel location of the target in an image, with measurements of MAV position and attitude, and camera pose angles, the target is localized in world coordinates. The main contribution of this paper is to present four techniques for reducing the localization error. In particular, we discuss RLS filtering, bias estimation, flight path selection, and wind estimation. The localization method has been implemented and flight tested on BYU's MAV testbed and experimental results are presented demonstrating the localization of a target to within 3 meters of its known GPS location.

The unprecedented volume and rate of transient events that will be discovered by the Large Synoptic Survey Telescope (LSST) demands that the astronomical community update its followup paradigm. Alert-brokers -automated software system to sift through, characterize, annotate and prioritize events for followup -will be critical tools for managing alert streams in the LSST era. The Arizona-NOAO Temporal Analysis and Response to Events System (ANTARES) is one such broker. In this work, we develop a machine learning pipeline to characterize and classify variable and transient sources only using the available multiband optical photometry. We describe three illustrative stages of the pipeline, serving the three goals of early, intermediate and retrospective classification of alerts. The first takes the form of variable vs transient categorization, the second, a multi-class typing of the combined variable and transient dataset, and the third, a purity-driven subtyping of a transient class. While several similar algorithms have proven themselves in simulations, we validate their performance on real observations for the first time. We quantitatively evaluate our pipeline on sparse, unevenly sampled, heteroskedastic data from various existing observational campaigns, and demonstrate very competitive classification performance. We describe our progress towards adapting the pipeline developed in this work into a real-time broker working on live alert streams from time-domain surveys.

This paper presents a method for localizing a ground-based object when imaged from a small fixed-wing unmanned aerial vehicle (UAV). Using the pixel location of the target in an image, with measurements of UAV position and attitude, and camera pose angles, the target is localized in world coordinates. This paper presents a study of possible error sources and localization sensitivities to each source. The localization method has been implemented and experimental results are presented demonstrating the localization of a target to within 11 m of its known location.

In this paper we investigate the nonlinear observability properties of bearing-only cooperative localization. We establish a link between observability and a graph representing measurements and communication between the robots. It is shown that graph theoretic properties like the connectivity and the existence of a path between two nodes can be used to explain the observability of the system. We obtain the maximum rank of the observability matrix without global information and derive conditions under which the maximum rank can be achieved. Furthermore, we show that for complete observability, all of the nodes in the graph must have a path to at least two different landmarks of known location.

This paper discusses a computer vision algorithm and a control law for obstacle avoidance for small unmanned air vehicles using a video camera as the primary sensor. Small UAVs are used for low altitude surveillance flights where unknown obstacles can be encountered. Small UAVs can be given the capability to navigate in uncertain environments if obstacles are identified. This paper presents an obstacle detection methodology using feature tracking in a forward looking, onboard camera. Features are found using the Harris Corner Detector and tracked through multiple video frames which provides three dimensional localization of the salient features. A sparse three dimensional map of features provides a rough estimate of obstacle locations. The features are grouped into potentially problematic areas using agglomerative clustering. The small UAV then employs a sliding mode control law in the autopilot to avoid obstacles.