Abstract: A method and system provide for temporal fusion of depth maps in an image space representation. A series of depth maps are obtained/acquired from one or more depth sensors at a first time. A first Gaussian mixture model (GMM) is initialized using one of the series of depth maps. A second depth map is obtained from the depth sensors at a second time. An estimate of the motion of the depth sensors, from the first time to the second time, is received. A predictive GMM at the second time is created based on a transform of the first GMM and the estimate of the motion. The predictive GMM is updated based on the second depth map.

Abstract: A method, device, framework, and system provide the ability to control an unmanned aerial vehicle (UAV) to avoid obstacle collision. Range data of a real-world scene is acquired using range sensors (that provide depth data to visible objects). The range data is combined into an egospace representation (consisting of pixels in egospace). An apparent size of each of the visible objects is expanded based on a dimension of the UAV. An assigned destination in the real world scene based on world space is received and transformed into egospace coordinates in egospace. A trackable path from the UAV to the assigned destination through egospace that avoids collision with the visible objects (based on the expanded apparent sizes of each of the visible objects) is generated. Inputs that control the UAV to follow the trackable path are identified.

Abstract: A method and system provide for temporal fusion of depth maps in an image space representation. A series of depth maps are obtained/acquired from one or more depth sensors at a first time. A first Gaussian mixture model (GMM) is initialized using one of the series of depth maps. A second depth map is obtained from the depth sensors at a second time. An estimate of the motion of the depth sensors, from the first time to the second time, is received. A predictive GMM at the second time is created based on a transform of the first GMM and the estimate of the motion. The predictive GMM is updated based on the second depth map.

Abstract: A method, device, framework, and system provide the ability to control an unmanned aerial vehicle (UAV) to avoid obstacle collision. Range data of a real-world scene is acquired using range sensors (that provide depth data to visible objects). The range data is combined into an egospace representation (consisting of pixels in egospace). An apparent size of each of the visible objects is expanded based on a dimension of the UAV. An assigned destination in the real world scene based on world space is received and transformed into egospace coordinates in egospace. A trackable path from the UAV to the assigned destination through egospace that avoids collision with the visible objects (based on the expanded apparent sizes of each of the visible objects) is generated. Inputs that control the UAV to follow the trackable path are identified.

Abstract: Systems and methods are disclosed that include automated machine vision that can utilize images of scenes captured by a 3D imaging system configured to image light within the visible light spectrum to detect water. One embodiment includes autonomously detecting water bodies within a scene including capturing at least one 3D image of a scene using a sensor system configured to detect visible light and to measure distance from points within the scene to the sensor system, and detecting water within the scene using a processor configured to detect regions within each of the at least one 3D images that possess at least one characteristic indicative of the presence of water.

Abstract: Systems and methods are disclosed that include automated machine vision that can utilize images of scenes captured by a 3D imaging system configured to image light within the visible light spectrum to detect water. One embodiment includes autonomously detecting water bodies within a scene including capturing at least one 3D image of a scene using a sensor system configured to detect visible light and to measure distance from points within the scene to the sensor system, and detecting water within the scene using a processor configured to detect regions within each of the at least one 3D images that possess at least one characteristic indicative of the presence of water.

Abstract: Apparatus and methods for near real-time stereo vision system for use with a robotic vehicle comprises two cameras mounted on three-axis rotation platforms, image-processing boards, a CPU, and specialized stereo vision algorithms. Bandpass-filtered image pyramids are computed, stereo matching is performed by least-squares correlation, and confidence ranges are estimated by means of Bayes' theorem. In particular, Laplacian image pyramids are built and disparity maps are produced from the 60.times.64 level of the pyramids at rates of up to 2 seconds per image pair. The first autonomous cross-country robotic traverses (of up to 100 meters) have been achieved using the stereo vision system of the present invention with all computing due aboard the vehicle. The overall approach disclosed herein provides a unifying paradigm for practical domain-independent stereo ranging.