MAGIC 2010

The APRIL laboratory won the Multi Autonomous Ground Robot International Challenge (MAGIC) Robotics competition, besting 22 other teams from around the world. To do this, we developed a team of robots that can explore an urban environment (indoors and outdoors), identify and track people, and identify objects of interests.

Grade Crossing Safety

A grade crossing is a crossing of a railway line and a motor road. In 2009 alone there were 248 deaths and 682 injuries at grade crossings in the United States. Factors like the elevation profile of a crossing or the environment and foliage around the crossing can render it unsafe. Often, vehicles with low ground clearance bottom out on a crossing with a humped elevation profile. Excessive foliage around the crossing can obstruct the visibility of an approaching train, reducing the time a driver has to stop. Hence ensuring safety requires regular monitoring and timely maintenance of grade crossings across the country.

We are building systems that will automatically determine whether a grade crossing is unsafe. Our system builds a 3D model of a grade crossing using LIDAR and camera data, from which it can measure the critical safety parameters of the crossing.

Graph-based Segmentation of Colored 3D Point Clouds

Robots navigating and interpreting a complex environment depend both on its spatial layout and its appearance. Traditional sensors measure either spatial information (e.g. laser scanners) or appearance (e.g. cameras). We enable the creation of a rich 3D data source which combines spatial and color information by accurately co-registering a camera with an actuated planar laser scanner.

Segmentation is an important pre-processing step necessary for enabling both high-level object identification and terrain classification. We demonstrate a novel segmentation method which can deal correctly with joint color and spatial information. Our method works on both indoor and outdoor scenes and produces segments which can include gradient regions and areas of uniform variance.

Probabilistic adversarial pursuit

The pursuit-evasion problem consists of a team of pursuers maneuvering to capture one or more evaders. Solutions to the pursuit-evasion problem help search and rescue teams quickly find survivors, sentries protect against intruders, and law enforcement apprehend suspects.

We've developed new probabilistic methods that model the cunning of an evader. This allows pursuers to be more conservative when pursuing smart evaders, and more aggressive when pursuing naive evaders.