Charles E. Thorpe

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LOCALIZATION, mapping and moving object tracking serve as the basis for scene understanding, which is a key prerequisite for making a robot truly autonomous. Simultaneous localization, mapping and moving object tracking (SLAMMOT) involves not only simultaneous localization and mapping (SLAM) in dynamic environments but also detecting and tracking these(More)
Despite advances in autonomy, there will always be a need for human involvement in vehicle teleoperation. In particular, tasks such as exploration, reconnaissance and surveillance will continue to require human supervision, if not guidance and direct control. Thus, it is critical that the operator interface be as efficient and as capable as possible. In(More)
In this paper, we present a real-time pedestrian detection system that uses a pair of moving cameras to detect both stationary and moving pedestrians in crowded environments. This is achieved through stereo-based segmentation and neural network-based recognition. Stereo-based segmentation allows us to extract objects from a changing background; neural(More)
At Camegie Mellon University, we have two new vision systems for outdoor road following. The first system, called SCARF (Supervised Classification Applied to Road Following), is designed to be fast and robust when the vehicle is running in both sunshine and shadows under constant illumination. The second system, UNSCARF (UNSupervised Classification Applied(More)
− The simultaneous localization and mapping (SLAM) with detection and tracking of moving objects (DATMO) problem is not only to solve the SLAM problem in dynamic environments but also to detect and track these dynamic objects. In this paper, we derive the Bayesian formula of the SLAM with DATMO problem, which provides a solid basis for understanding and(More)
A method is presented to recover 3D scene structure and camera motion from multiple images without the need for correspondence information. The problem is framed as finding the maximum likelihood structure and motion given only the 2D measurements, integrating over all possible assignments of 3D features to 2D measurements. This goal is achieved by means of(More)
Multi-robot remote driving has traditionally been a difficult problem. Whenever an operator is forced to divide his limited resources (attention, cognition, etc.) among multiple robots, control becomes complicated and performance deteriorates as a result. Thus, we need to find ways to make command generation and coordination efficient, so that human-robot(More)
Collaborative control is a teleoperation system model based on human–robot dialogue. With this model, the robot asks questions to the human in order to obtain assistance with cognition and perception. This enables the human to function as a resource for the robot and help to compensate for limitations of autonomy. To understand how collaborative control(More)