Active epipolar imaging simultaneously illuminates and images a scene along epipolar planes. The recent devices based on this principle, such as Episcan [1] and EpiToF [2], significantly reduce the effects of indirect light transport and ambient light, resulting in live capture of high resolution 3D at longer ranges indoors and outdoors. However, these devices are designed for narrow fields of view where lens distortion can be ignored and epipolar plane/line constraints are satisfied. In this work, we extend active epipolar imaging to obtain live-omnidirectional stereo for the first time. Instead of using a 2D sensor/projector, we use a 1D sensor and 1D light sheet source that are placed in a rectified configuration. We observe that when the lens center axis and the sensor are aligned, the distortion is mostly along that axis and epipolar plane constraint remains satisfied. This allows us to use small wide-angle lenses resulting in a compact 1D Episcan. This 1D Episcan is then spun quickly to obtain an near-spherical field of view. Based on this design, we demonstrate a custom-built hand-held working prototype for live capture of omni-directional active stereo images.

High-mobility walking robots offer unique capabilities in complex off-road environments where wheeled vehicles are not able to travel. However, these environments can also pose significant autonomous navigation challenges. Key steps in planning a safe path for the robot autonomously include estimating the height of the support ground surface - which is often occluded by vegetation - and classifying the terrain and obstacles above the ground surface. This paper describes the development and experimental evaluation of a terrain classification and ground surface height estimation system to support autonomous navigation for a high-mobility walking robot. We provide experimental evaluation on an extensive, manually-labeled dataset collected from geographically diverse sites over a 28-month period.

A key challenge of developing robots that work closely with people is creating a user interface that allows a user to communicate complex instructions to a robot quickly and easily. We consider a walking logistics support robot, which is designed to carry heavy loads to locations that are too difficult to reach with a wheeled or tracked vehicle. In this application the robot is carrying equipment and supplies for a group of pedestrians, and the primary task for the user interface is to keep the robot traveling with the overall group in the right formation. This paper presents a marker tracking system that uses near infrared cameras, retro-reflective markers, and LIDAR to allow a particular user to designate himself as the robot's leader, and guide the robot along a desired path. We provide an extensive quantitative evaluation to show that the proposed system is able to detect and track a leader through unconstrained and cluttered off-road environments under a wide variety of illumination and motion conditions.

Algorithms for shape-from-motion simultaneously estimate the camera motion and scene structure. When extended to omnidirectional cameras, shape-from-motion algorithms are likely to provide robust motion estimates, in particular, because of the camera's wide field of view. In this paper, we describe both batch and online shape-from-motion algorithms for omnidirectional cameras, and a precise calibration technique that improves the accuracy of both methods. The shape-from-motion and calibration methods are general, and they handle a wide variety of omnidirectional camera geometries. In particular, the methods do not require that the camera-mirror combination have a single center of projection. We describe a noncentral camera that we have developed, and show experimentally that combining shape-from-motion with this design produces highly accurate motion estimates.

We have developed an autonomous system for the retrieval of buried objects. It is designed to detect, locate and retrieve buried objects. The system is equipped with a hydraulic robot, laser range finder and a subsurface sensor. First, subsurface sensing is used to detect and locate buried objects. If an object can be reached with one dig, the excavator retrieves it directly. Otherwise a layer of soil above the object is removed and another subsurface scan is made to get a more accurate estimate of the object location. This loop is repeated until the object is retrieved. The paper presents some recent results in sensing and excavation from experiments conducted on our testbed. There are numerous potential applications for this system-hazardous waste removal, maintenance of subsurface structure, construction, and removal of unexploded buried ordnance.>