Expeditionary environments create special challenges for perception systems in autonomous ground vehicles. To address these challenges, a perception pipeline has been developed that fuses data from multiple sensors (color, thermal, LIDAR) with different sensing modalities and spatial resolutions. The paper begins with in-depth discussion of the multi-sensor calibration procedure. It then follows the flow of data through the perception pipeline, detailing the process by which the sensor data is combined in the world model representation. Topics of interest include stereo filtering, stereo and LIDAR ground segmentation, pixel classification, 3D occupancy grid aggregation, and costmap generation
Video surveillance is ubiquitous in modern society, but surveillance cameras are severely limited in utility by their low
resolution. With this in mind, we have developed a system that can autonomously take high resolution still frame
images of moving objects. In order to do this, we combine a low resolution video camera and a high resolution still
frame camera mounted on a pan/tilt mount. In order to determine what should be photographed (objects of interest), we
employ a hierarchical method which first separates foreground from background using a temporal-based median
filtering technique. We then use a feed-forward neural network classifier on the foreground regions to determine
whether the regions contain the objects of interest. This is done over several frames, and a motion vector is deduced for
the object. The pan/tilt mount then focuses the high resolution camera on the next predicted location of the object, and
an image is acquired. All components are controlled through a single MATLAB graphical user interface (GUI). The
final system we present will be able to detect multiple moving objects simultaneously, track them, and acquire high
resolution images of them. Results will demonstrate performance tracking and imaging varying numbers of objects
moving at different speeds.
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