Stereo and ToF Data Fusion by Learning from Synthetic Data

Time-of-Flight (ToF) sensors and stereo vision systems are both capable of acquiring depth information but they have complementary characteristics and issues. A more accurate representation of the scene geometry can be obtained by fusing the two depth sources. In this paper we present a novel framework for data fusion where the contribution of the two depth sources is controlled by confidence measures that are jointly estimated using a Convolutional Neural Network. The two depth sources are fused enforcing the local consistency of depth data, taking into account the estimated confidence information. The deep network is trained using a synthetic dataset and we show how the classifier is able to generalize to different data, obtaining reliable estimations not only on synthetic data but also on real world scenes. Experimental results show that the proposed approach increases the accuracy of the depth estimation on both synthetic and real data and that it is able to outperform state-of-the-art methods

Reliable fusion of ToF and stereo depth driven by confidence measures

In this paper we propose a framework for the fusion of depth data produced by a Time-of-Flight (ToF) camera and stereo vision system. Initially, depth data acquired by the ToF camera are upsampled by an ad-hoc algorithm based on image segmentation and bilateral filtering. In parallel a dense disparity map is obtained using the Semi- Global Matching stereo algorithm. Reliable confidence measures are extracted for both the ToF and stereo depth data. In particular, ToF confidence also accounts for the mixed-pixel effect and the stereo confidence accounts for the relationship between the pointwise matching costs and the cost obtained by the semi-global optimization. Finally, the two depth maps are synergically fused by enforcing the local consistency of depth data accounting for the confidence of the two data sources at each location. Experimental results clearly show that the proposed method produces accurate high resolution depth maps and outperforms the compared fusion algorithms

Toward Automated Aerial Refueling: Relative Navigation with Structure from Motion

The USAF\u27s use of UAS has expanded from reconnaissance to hunter/killer missions. As the UAS mission further expands into aerial combat, better performance and larger payloads will have a negative correlation with range and loiter times. Additionally, the Air Force Future Operating Concept calls for \formations of uninhabited refueling aircraft...[that] enable refueling operations partway inside threat areas. However, a lack of accurate relative positioning information prevents the ability to safely maintain close formation flight and contact between a tanker and a UAS. The inclusion of cutting edge vision systems on present refueling platforms may provide the information necessary to support a AAR mission by estimating the position of a trailing aircraft to provide inputs to a UAS controller capable of maintaining a given position. This research examines the ability of SfM to generate relative navigation information. Previous AAR research efforts involved the use of differential GPS, LiDAR, and vision systems. This research aims to leverage current and future imaging technology to compliment these solutions. The algorithm used in this thesis generates a point cloud by determining 3D structure from a sequence of 2D images. The algorithm then utilizes PCA to register the point cloud to a reference model. The algorithm was tested in a real world environment using a 1:7 scale F-15 model. Additionally, this thesis studies common 3D rigid registration algorithms in an effort characterize their performance in the AAR domain. Three algorithms are tested for runtime and registration accuracy with four data sets

A New Passive 3-D Automatic Target Recognition Architecture for Aerial Platforms

The 3-D automatic target recognition (ATR) has many advantages over its 2-D counterpart, but there are several constraints in the context of small low-cost unmanned aerial vehicles (UAVs). These limitations include the requirement for active rather than passive monitoring, high equipment costs, sensor packaging size, and processing burden. We, therefore, propose a new structure from motion (SfM) 3-D ATR architecture that exploits the UAV's onboard sensors, i.e., the visual band camera, gyroscope, and accelerometer, and meets the requirements of a small UAV system. We tested the proposed 3-D SfM ATR using simulated UAV reconnaissance scenarios and found that the performance was better than classic 3-D light detection and ranging (LIDAR) ATR, combining the advantages of 3-D LIDAR ATR and passive 2-D ATR. The main advantages of the proposed architecture include the rapid processing, target pose invariance, small template size, passive scene sensing, and inexpensive equipment. We implemented the SfM module under two keypoint detection, description and matching schemes, with the 3-D ATR module exploiting several current techniques. By comparing SfM 3-D ATR, 3-D LIDAR ATR, and 2-D ATR, we confirmed the superior performance of our new architecture

Probabilistic three-dimensional object tracking based on adaptive depth segmentation

Object tracking is one of the fundamental topics of computer vision with diverse applications. The arising challenges in tracking, i.e., cluttered scenes, occlusion, complex motion, and illumination variations have motivated utilization of depth information from 3D sensors. However, current 3D trackers are not applicable to unconstrained environments without a priori knowledge. As an important object detection module in tracking, segmentation subdivides an image into its constituent regions. Nevertheless, the existing range segmentation methods in literature are difficult to implement in real-time due to their slow performance. In this thesis, a 3D object tracking method based on adaptive depth segmentation and particle filtering is presented. In this approach, the segmentation method as the bottom-up process is combined with the particle filter as the top-down process to achieve efficient tracking results under challenging circumstances. The experimental results demonstrate the efficiency, as well as robustness of the tracking algorithm utilizing real-world range information

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 190, February 1979

This bibliography lists 235 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1979

3D data fusion from multiple sensors and its applications

The introduction of depth cameras in the mass market contributed to make computer vision applicable to many real world applications, such as human interaction in virtual environments, autonomous driving, robotics and 3D reconstruction. All these problems were originally tackled by means of standard cameras, but the intrinsic ambiguity in the bidimensional images led to the development of depth cameras technologies. Stereo vision was first introduced to provide an estimate of the 3D geometry of the scene. Structured light depth cameras were developed to use the same concepts of stereo vision but overcome some of the problems of passive technologies. Finally, Time-of-Flight (ToF) depth cameras solve the same depth estimation problem by using a different technology. This thesis focuses on the acquisition of depth data from multiple sensors and presents techniques to efficiently combine the information of different acquisition systems. The three main technologies developed to provide depth estimation are first reviewed, presenting operating principles and practical issues of each family of sensors. The use of multiple sensors then is investigated, providing practical solutions to the problem of 3D reconstruction and gesture recognition. Data from stereo vision systems and ToF depth cameras are combined together to provide a higher quality depth map. A confidence measure of depth data from the two systems is used to guide the depth data fusion. The lack of datasets with data from multiple sensors is addressed by proposing a system for the collection of data and ground truth depth, and a tool to generate synthetic data from standard cameras and ToF depth cameras. For gesture recognition, a depth camera is paired with a Leap Motion device to boost the performance of the recognition task. A set of features from the two devices is used in a classification framework based on Support Vector Machines and Random Forests