We present a camera system for instantaneous, non-destructive capture of spectral signatures for forensic analysis. Our
system detects highly probative samples in the forensic scene mixed by the multiple target objects by combining a coded
aperture snapshot spectral imager with a multi-spectral detection algorithm. An Adaptive Cosine Estimator (ACE) is
used to quantitatively detect and classify the probative samples from the decoded spectral datacube. In this paper, we
demonstrate selected results using our system for luminescence characteristics and spectral classification of a number of
samples.
Autonomous robotic “fetch” operation, where a robot is shown a novel object and then asked to locate it in the field, re-
trieve it and bring it back to the human operator, is a challenging problem that is of interest to the military. The CANINE
competition presented a forum for several research teams to tackle this challenge using state of the art in robotics technol-
ogy. The SRI-UPenn team fielded a modified Segway RMP 200 robot with multiple cameras and lidars. We implemented
a unique computer vision based approach for textureless colored object training and detection to robustly locate previ-
ously unseen objects out to 15 meters on moderately flat terrain. We integrated SRI’s state of the art Visual Odometry for
GPS-denied localization on our robot platform. We also designed a unique scooping mechanism which allowed retrieval
of up to basketball sized objects with a reciprocating four-bar linkage mechanism. Further, all software, including a novel
target localization and exploration algorithm was developed using ROS (Robot Operating System) which is open source
and well adopted by the robotics community. We present a description of the system, our key technical contributions and
experimental results.
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