The Army Research Laboratory (ARL) Robotics Collaborative Technology Alliance (CTA) conducted an assessment and evaluation of multiple algorithms for real-time detection of pedestrians in Laser Detection and Ranging (LADAR) and video sensor data taken from a moving platform. The algorithms were developed by Robotics CTA members and then assessed in field experiments jointly conducted by the National Institute of Standards and Technology (NIST) and ARL. A robust, accurate and independent pedestrian tracking system was developed to provide ground truth. The ground truth was used to evaluate the CTA member algorithms for uncertainty and error in their results. A real-time display system was used to provide early detection of errors in data collection.
The Army Research Laboratory Robotic Collaborative Technology Alliance (RCTA) made significant advances in perception (LADAR and near and mid-term perception algorithms) and planning (global/local route planning, shared map data, and the ability to use time as a planning factor) which enabled true UGV autonomous navigation (planning and controlling the course and position of a vehicle) [1,3]. This opened the door to a myriad of operational-like capabilities which we call tactical behaviors. The purpose of this paper is to provide an overview of the assessment process with a focus on performance metrics and to foster collaboration with other investigators in order to further derive meaningful metrics for UGV autonomous navigation.
ARL is developing the autonomous capability to directly support the Army's future requirements to employ unmanned systems. The purpose of this paper is to document and benchmark the current ARL Collaborative Technology Alliance (CTA) capabilities in detecting, tracking and avoiding moving humans and vehicles from a moving unmanned vehicle. For this experiment ARL and General Dynamics Robotic Systems (GDRS) conducted an experiment involving an ARL eXperimental Unmanned Vehicle (XUV) operating in proximity to a number of stationary and moving human surrogates (mannequins) and moving vehicles. In addition there were other objects along the XUV route of the experiment such as barrels, fire hydrants, poles, cones, and other clutter.The experiment examined the performance of seven algorithms using a series of sensor modalities to detect stationary and moving objects. Three of the algorithms showed promise, detecting human surrogates and vehicles with probabilities ranging from 0.64 to 0.85, while limiting probability of misclassification to 0.14 to 0.37. Moving mannequins were detected with slightly higher probabilities than fixed mannequins. The distance from the ground truth at the time of detection suggests that at a speed of 20 kph with a minimum distance to detection of 19.38 m, the vehicle would have a minimum of 3.5 seconds to avoid a mannequin or vehicle if detected by one of these three algorithms. Among mannequins and vehicles and, mannequins were more frequently detected than vehicles.
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