An accurate star identification approach based on spectral graph matching for attitude measurement of spacecraft

Star identification is the foundation of star trackers, which are used to precisely determine the attitude of spacecraft. In this paper, we propose a novel star identification approach based on spectral graph matching. In the proposed approach, we construct a feature called the neighbor graph for each main star, transforming the star identification to the problem of finding the most similar neighbor graph. Then the rough search and graph matching are cooperated to form a dynamic search framework to solve the problem. In the rough search stage, the total edge weight in the minimum spanning tree of the neighbor graph is selected as an indicator, then the k-vector range search is applied for reducing the search scale. Spectral graph matching is utilized to achieve global matching, identifying all stars in the neighbor circle with good noise-tolerance ability. Extensive simulation experiments under the position noise, lost-star noise, and fake-star noise show that our approach achieves higher accuracy (mostly over 99%) and better robustness results compared with other baseline algorithms in most cases.

Tracking induced forgetting across both strong and weak memory representations to test competing theories of forgetting

Here we employ a novel analysis to address the question: what causes induced forgetting of pictures? We use baseline memorability as a measure of initial memory strength to ask whether induced forgetting is due to (1) recognition practice damaging the association between the memory representation and the category cue used to activate the representation, (2) the updating of a memory trace by incorporating information about a memory probe presented during recognition practice to the stored trace, (3) inhibitory mechanisms used to resolve the conflict created when correctly selecting the practiced item activates competing exemplars, (4) a global matching model in which repeating some items will hurt memory for other items, or (5) falling into the zone of destruction, where a moderate amount of activation leads to the highest degree of forgetting. None of the accounts of forgetting tested here can comprehensively account for both the novel analyses reported here and previous data using the induced forgetting paradigm. We discuss aspects of forgetting theories that are consistent with the novel analyses and existing data, a potential solution for existing models, proposals for future directions, and considerations when incorporating memorability into models of memory.

Real-time stereo semi-global matching for video processing using previous incremental information

This paper presents an incremental stereo algorithm designed to calculate a real-time disparity image. The algorithm is designed for stereo video sequences and uses previous information to reduce computation time and improve disparity image quality. It is based on the semi-global matching stereo algorithm but modified to reuse previous calculation information. Storing and reusing this information not only reduces computation time but improves accuracy in a cost filtering scheme. Some tests are presented to compare the computation time and results of the algorithm, which show that it can achieve better results in terms of quality and time than standard algorithms for some scenarios.

AMBIGUITY CONCEPT IN STEREO MATCHING PIPELINE

In a 3D reconstruction pipeline, stereo matching step aims at computing a disparity map representing the depth between image pair. The evaluation of the disparity map can be done through the estimation of a confidence metric. In this article, we propose a new confidence metric, named ambiguity integral metric, to assess the quality of the produced disparity map. This metric is derived from the concept of ambiguity, which characterizes the property of the cost curve profile. It aims to quantify the difficulty in identifying the correct disparity to select. The quality of ambiguity integral metric is evaluated through the ROC curve methodology and compared with other confidence measures. In regards to other measures, the ambiguity integral measure shows a good potential. We also integrate this measure through various steps of the stereo matching pipeline in order to improve the performance estimation of the disparity map. First, we include ambiguity integral measure during the Semi Global Matching optimization step. The objective is to weight, by ambiguity integral measure, the influence of points in the SGM regularization to reduce the impact of ambiguous points. Secondly, we use ambiguity as an input of a disparity refinement deep learning architecture in order to easily locate noisy area and preserve details.

DIGITAL ELEVATION MODELS FROM STEREO, VIDEO AND MULTI-VIEW IMAGERY CAPTURED BY SMALL SATELLITES

Small satellites play an increasing role in earth observation. This article evaluates different possibilities of utilizing data from Planet’s SkySat and PlanetScope satellites constellations for derivation of digital elevation models. While SkySat provides high resolution image data with a ground sampling distance of up to 50 cm, the PlanetScope constellation consisting of Dove 3U cubesats provide images with a resolution of around 4 m. The PlanetScope acquisition strategy was not designed for stereo acquisitions, but for daily acquisition of nadir viewing imagery. Multiple different products can be acquired by the SkySat satellites: Collects covering an area of usually 12 by 6 km, tri-stereo collects and video products with a framerate of 30 Hz. This study evaluates DSM generation using a Semi-Global Matching from multi date stereo pairs for SkySat and PlanetScope, and the dedicated Video and tri-stereo SkySat acquisitions. DSMs obtained by merging many PlanetScope across track stereo pairs show an normalized median deviation against LiDaR first pulse data of 5.2 meter over diverse landcover at the test sites around the city of Terrassa in Catalonia, Spain. SkySat tri-stereo products with 80 cm resolution reach an NMAD of 1.3 m over Terrassa.

REAL-TIME DEPTH MAP ESTIMATION FROM INFRARED STEREO IMAGES OF RGB-D CAMERAS

RGB-D cameras are novel sensing systems that can rapidly provide accurate depth information for 3D perception, among which the type based on active stereo vision has been widely used. However, there are some problems exiting in use, such as the short measurement range and incomplete depth maps. This paper presents a robust and efficient matching algorithm based on semi-global matching to obtain more complete and accurate depth maps in real time. Considering characteristics of captured infrared speckle images, the Gaussian filter is performed firstly to restrain noise and enhance the relativity. It also adopts the idea of block matching for reliability, and a dynamic threshold selection of the block size is used to adapt to various situation. Moreover, several optimizations are applied to improve precision and reduce error. Through experiments on the Intel Realsense R200, the excellent capability of our proposed method is verified.

ReS2tAC—UAV-Borne Real-Time SGM Stereo Optimized for Embedded ARM and CUDA Devices

With the emergence of low-cost robotic systems, such as *UAV, the importance of embedded high-performance image processing has increased. For a long time, FPGAs were the only processing hardware that were capable of high-performance computing, while at the same time preserving a low power consumption, essential for embedded systems. However, the recently increasing availability of embedded GPU-based systems, such as the NVIDIA Jetson series, comprised of an ARM CPU and a NVIDIA Tegra GPU, allows for massively parallel embedded computing on graphics hardware. With this in mind, we propose an approach for real-time embedded stereo processing on ARM and CUDA-enabled devices, which is based on the popular and widely used Semi-Global Matching algorithm. In this, we propose an optimization of the algorithm for embedded CUDA GPUs, by using massively parallel computing, as well as using the NEON intrinsics to optimize the algorithm for vectorized SIMD processing on embedded ARM CPUs. We have evaluated our approach with different configurations on two public stereo benchmark datasets to demonstrate that they can reach an error rate as low as 3.3%. Furthermore, our experiments show that the fastest configuration of our approach reaches up to 46 FPS on VGA image resolution. Finally, in a use-case specific qualitative evaluation, we have evaluated the power consumption of our approach and deployed it on the DJI Manifold 2-G attached to a DJI Matrix 210v2 RTK *UAV, demonstrating its suitability for real-time stereo processing onboard a *UAV.