Occlusion-Robust MVO: Multimotion Estimation Through Occlusion Via Motion Closure

Visual motion estimation is an integral and well-studied challenge in autonomous navigation. Recent work has focused on addressing multimotion estimation, which is especially challenging in highly dynamic environments. Such environments not only comprise multiple, complex motions but also tend to exhibit significant occlusion. Previous work in object tracking focuses on maintaining the integrity of object tracks but usually relies on specific appearance-based descriptors or constrained motion models. These approaches are very effective in specific applications but do not generalize to the full multimotion estimation problem. This paper presents a pipeline for estimating multiple motions, including the camera egomotion, in the presence of occlusions. This approach uses an expressive motion prior to estimate the SE (3) trajectory of every motion in the scene, even during temporary occlusions, and identify the reappearance of motions through motion closure. The performance of this occlusion-robust multimotion visual odometry (MVO) pipeline is evaluated on real-world data and the Oxford Multimotion Dataset.Comment: To appear at the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). An earlier version of this work first appeared at the Long-term Human Motion Planning Workshop (ICRA 2019). 8 pages, 5 figures. Video available at https://www.youtube.com/watch?v=o_N71AA6FR

Identifying prognostic indicators for electrical treeing in solid insulation through pulse sequence analysis

Predictive maintenance attempts to evaluate the condition of equipment and predict the future trend of the equipment's aging, in order to reduce costs when compared to the two traditional approaches: corrective and preventive maintenance. This prediction requires an accurate prognostic model of aging. In solid insulation, the ultimate goal of prognostics is to predict the advent of failure, i.e., insulation breakdown, in terms of remaining useful life (RUL). One fault is electrical treeing, which is progressive thus leading to potentially catastrophic failure. Research has shown that diagnosis of faults can be achieved based on partial discharge (PD) monitoring [1], i.e., phase-resolved and pulse sequence analysis (PSA). This work will explore the extension of this concept towards predicting evolution of the defect: moving beyond diagnostics towards prognostics. To do this, there is a need for further investigation of prognostic features within PD characteristics leading up to breakdown. In this work, a needle-plane test arrangement was set up using a hypodermic needle and pre-formed silicone rubber as test samples. The visual observations and tree growth measurements were made using a digital microscope. PD data was captured using a radio frequency (RF) sensor and analysed using PSA. The main idea of the PSA approach is the strong relationship between two consecutive pulses caused by PD activities, which can give an understanding of the local degradation processes [1]. As for electrical treeing, a breakdown indicator in PSA is the appearance of heavily clustered data points that lie diagonally in scatter plots of the differential ratio of voltage and time of consecutive charges (Un = Δun/Δtn) [2,3]. Figure 1 shows an example of a plot that changed to a diagonal line after 14 hours of aging time. This paper investigates the formation of the diagonal line based on the distribution of the plot from the start of electrical treeing until breakdown occurs. Finally, statistical features of the PSA plot are given and will be used for lifetime prediction of insulation samples in future work

Data management of on-line partial discharge monitoring using wireless sensor nodes integrated with a multi-agent system

On-line partial discharge monitoring has been the subject of significant research in previous years but little work has been carried out with regard to the management of on-site data. To date, on-line partial discharge monitoring within a substation has only been concerned with single plant items, so the data management problem has been minimal. As the age of plant equipment increases, so does the need for condition monitoring to ensure maximum lifespan. This paper presents an approach to the management of partial discharge data through the use of embedded monitoring techniques running on wireless sensor nodes. This method is illustrated by a case study on partial discharge monitoring data from an ageing HVDC reactor

Implications of non-feasible transformations among icosahedral hh orbitals

The symmetric group $S_6$ that permutes the six five-fold axes of an icosahedron is introduced to go beyond the simple rotations that constitute the icosahedral group $I$. Owing to the correspondence $h\leftrightarrow d$, the calculation of the Coulomb energies for the icosahedral configurations $h^N$ based on the sequence $O(5) \supset S_6 \supset S_5 \supset I$ can be brought to bear on Racah's classic theory for the atomic d shell based on $SO(5) \supset SO_L(3) \supset I$. Among the elements of $S_6$ is the kaleidoscope operator ${\cal K}$ that rotates the weight space of SO(5) by $\pi/2$. Its use explains some puzzling degeneracies in d^3 involving the spectroscopic terms ^2P, ^2F, ^2G and ^2H.Comment: Tentatively scheduled to appear in Physical Preview Letters Apr 5, 99. Revtex, 1 ps figur

Quantifying Finite Temperature Effects in Atom Chip Interferometry of Bose-Einstein Condensates

We quantify the effect of phase fluctuations on atom chip interferometry of Bose-Einstein condensates. At very low temperatures, we observe small phase fluctuations, created by mean-field depletion, and a resonant production of vortices when the two clouds are initially in anti-phase. At higher temperatures, we show that the thermal occupation of Bogoliubov modes makes vortex production vary smoothly with the initial relative phase difference between the two atom clouds. We also propose a technique to observe vortex formation directly by creating a weak link between the two clouds. The position and direction of circulation of the vortices is subsequently revealed by kinks in the interference fringes produced when the two clouds expand into one another. This procedure may be exploited for precise force measurement or motion detection.Comment: 7 pages, 5 figure

Quantum reflection of ultracold atoms from thin films, graphene, and semiconductor heterostructures

We show that thin dielectric films can be used to enhance the performance of passive atomic mirrors by enabling quantum reflection probabilities of over 90% for atoms incident at velocities ~1 mm/s, achieved in recent experiments. This enhancement is brought about by weakening the Casimir-Polder attraction between the atom and the surface, which induces the quantum reflection. We show that suspended graphene membranes also produce higher quantum reflection probabilities than bulk matter. Temporal changes in the electrical resistance of such membranes, produced as atoms stick to the surface, can be used to monitor the reflection process, non-invasively and in real time. The resistance change allows the reflection probability to be determined purely from electrical measurements without needing to image the reflected atom cloud optically. Finally, we show how perfect atom mirrors may be manufactured from semiconductor heterostructures, which employ an embedded two-dimensional electron gas to tailor the atom-surface interaction and so enhance the reflection by classical means.Comment: 8 pages, 4 figure

The Oxford Multimotion Dataset: Multiple SE(3) Motions with Ground Truth

Datasets advance research by posing challenging new problems and providing standardized methods of algorithm comparison. High-quality datasets exist for many important problems in robotics and computer vision, including egomotion estimation and motion/scene segmentation, but not for techniques that estimate every motion in a scene. Metric evaluation of these multimotion estimation techniques requires datasets consisting of multiple, complex motions that also contain ground truth for every moving body. The Oxford Multimotion Dataset provides a number of multimotion estimation problems of varying complexity. It includes both complex problems that challenge existing algorithms as well as a number of simpler problems to support development. These include observations from both static and dynamic sensors, a varying number of moving bodies, and a variety of different 3D motions. It also provides a number of experiments designed to isolate specific challenges of the multimotion problem, including rotation about the optical axis and occlusion. In total, the Oxford Multimotion Dataset contains over 110 minutes of multimotion data consisting of stereo and RGB-D camera images, IMU data, and Vicon ground-truth trajectories. The dataset culminates in a complex toy car segment representative of many challenging real-world scenarios. This paper describes each experiment with a focus on its relevance to the multimotion estimation problem.Comment: 8 Pages. 8 Figures. Video available at https://www.youtube.com/watch?v=zXaHEdiKxdA. Dataset available at https://robotic-esp.com/datasets

Multimotion Visual Odometry (MVO)

Visual motion estimation is a well-studied challenge in autonomous navigation. Recent work has focused on addressing multimotion estimation in highly dynamic environments. These environments not only comprise multiple, complex motions but also tend to exhibit significant occlusion. Estimating third-party motions simultaneously with the sensor egomotion is difficult because an object's observed motion consists of both its true motion and the sensor motion. Most previous works in multimotion estimation simplify this problem by relying on appearance-based object detection or application-specific motion constraints. These approaches are effective in specific applications and environments but do not generalize well to the full multimotion estimation problem (MEP). This paper presents Multimotion Visual Odometry (MVO), a multimotion estimation pipeline that estimates the full SE(3) trajectory of every motion in the scene, including the sensor egomotion, without relying on appearance-based information. MVO extends the traditional visual odometry (VO) pipeline with multimotion segmentation and tracking techniques. It uses physically founded motion priors to extrapolate motions through temporary occlusions and identify the reappearance of motions through motion closure. Evaluations on real-world data from the Oxford Multimotion Dataset (OMD) and the KITTI Vision Benchmark Suite demonstrate that MVO achieves good estimation accuracy compared to similar approaches and is applicable to a variety of multimotion estimation challenges.Comment: Under review for the International Journal of Robotics Research (IJRR), Manuscript #IJR-21-4311. 25 pages, 14 figures, 11 tables. Videos available at https://www.youtube.com/watch?v=mNj3s1nf-6A and https://www.youtube.com/playlist?list=PLbaQBz4TuPcxMIXKh5Q80s0N9ISezFcp

Multimotion visual odometry

Visual motion estimation is a well-studied challenge in autonomous navigation. Recent work has focused on addressing multimotion estimation in highly dynamic environments. These environments not only comprise multiple, complex motions but also tend to exhibit significant occlusion. Estimating third-party motions simultaneously with the sensor egomotion is difficult because an object’s observed motion consists of both its true motion and the sensor motion. Most previous works in multimotion estimation simplify this problem by relying on appearance-based object detection or application-specific motion constraints. These approaches are effective in specific applications and environments but do not generalize well to the full multimotion estimation problem (MEP). This paper presents Multimotion Visual Odometry (MVO), a multimotion estimation pipeline that estimates the full SE(3) trajectory of every motion in the scene, including the sensor egomotion, without relying on appearance-based information. MVO extends the traditional visual odometry (VO) pipeline with multimotion segmentation and tracking techniques. It uses physically founded motion priors to extrapolate motions through temporary occlusions and identify the reappearance of motions through motion closure. Evaluations on real-world data from the Oxford Multimotion Dataset (OMD) and the KITTI Vision Benchmark Suite demonstrate that MVO achieves good estimation accuracy compared to similar approaches and is applicable to a variety of multimotion estimation challenges

Difference score correlations in relationship research: A conceptual primer

The practice of computing correlations between “difference” or “discrepancy” scores and an outcome variable is common in many areas of social science. Relationship researchers most commonly use difference scores to index the (dis)similarity of members of two-person relationships. Using an intuitive, graphical approach—and avoiding formulas and pointing fingers—we illustrate problems with using difference score correlations in relationship research, suggest ways to ensure that difference score correlations are maximally informative, and briefly review alternatives to difference score correlations in studying similarity, accuracy, and related constructs.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73008/1/j.1475-6811.1999.tb00206.x.pd