High accuracy simulations of black hole binaries:spins anti-aligned with the orbital angular momentum

High-accuracy binary black hole simulations are presented for black holes with spins anti-aligned with the orbital angular momentum. The particular case studied represents an equal-mass binary with spins of equal magnitude S/m^2=0.43757 \pm 0.00001. The system has initial orbital eccentricity ~4e-5, and is evolved through 10.6 orbits plus merger and ringdown. The remnant mass and spin are M_f=(0.961109 \pm 0.000003)M and S_f/M_f^2=0.54781 \pm 0.00001, respectively, where M is the mass during early inspiral. The gravitational waveforms have accumulated numerical phase errors of <~ 0.1 radians without any time or phase shifts, and <~ 0.01 radians when the waveforms are aligned with suitable time and phase shifts. The waveform is extrapolated to infinity using a procedure accurate to <~ 0.01 radians in phase, and the extrapolated waveform differs by up to 0.13 radians in phase and about one percent in amplitude from the waveform extracted at finite radius r=350M. The simulations employ different choices for the constraint damping parameters in the wave zone; this greatly reduces the effects of junk radiation, allowing the extraction of a clean gravitational wave signal even very early in the simulation.Comment: 14 pages, 15 figure

A Data-driven Model for Interaction-aware Pedestrian Motion Prediction in Object Cluttered Environments

This paper reports on a data-driven, interaction-aware motion prediction approach for pedestrians in environments cluttered with static obstacles. When navigating in such workspaces shared with humans, robots need accurate motion predictions of the surrounding pedestrians. Human navigation behavior is mostly influenced by their surrounding pedestrians and by the static obstacles in their vicinity. In this paper we introduce a new model based on Long-Short Term Memory (LSTM) neural networks, which is able to learn human motion behavior from demonstrated data. To the best of our knowledge, this is the first approach using LSTMs, that incorporates both static obstacles and surrounding pedestrians for trajectory forecasting. As part of the model, we introduce a new way of encoding surrounding pedestrians based on a 1d-grid in polar angle space. We evaluate the benefit of interaction-aware motion prediction and the added value of incorporating static obstacles on both simulation and real-world datasets by comparing with state-of-the-art approaches. The results show, that our new approach outperforms the other approaches while being very computationally efficient and that taking into account static obstacles for motion predictions significantly improves the prediction accuracy, especially in cluttered environments.Comment: 8 pages, accepted for publication at the IEEE International Conference on Robotics and Automation (ICRA) 201

A Data-driven Model for Interaction-aware Pedestrian Motion Prediction in Object Cluttered Environments

This paper reports on a data-driven, interaction-aware motion prediction approach for pedestrians in environments cluttered with static obstacles. When navigating in such workspaces shared with humans, robots need accurate motion predictions of the surrounding pedestrians. Human navigation behavior is mostly influenced by their surrounding pedestrians and by the static obstacles in their vicinity. In this paper we introduce a new model based on Long-Short Term Memory (LSTM) neural networks, which is able to learn human motion behavior from demonstrated data. To the best of our knowledge, this is the first approach using LSTMs, that incorporates both static obstacles and surrounding pedestrians for trajectory forecasting. As part of the model, we introduce a new way of encoding surrounding pedestrians based on a 1d-grid in polar angle space. We evaluate the benefit of interaction-aware motion prediction and the added value of incorporating static obstacles on both simulation and real-world datasets by comparing with state-of-the-art approaches. The results show, that our new approach outperforms the other approaches while being very computationally efficient and that taking into account static obstacles for motion predictions significantly improves the prediction accuracy, especially in cluttered environments.Comment: 8 pages, accepted for publication at the IEEE International Conference on Robotics and Automation (ICRA) 201

Dynamic Objects Segmentation for Visual Localization in Urban Environments

Visual localization and mapping is a crucial capability to address many challenges in mobile robotics. It constitutes a robust, accurate and cost-effective approach for local and global pose estimation within prior maps. Yet, in highly dynamic environments, like crowded city streets, problems arise as major parts of the image can be covered by dynamic objects. Consequently, visual odometry pipelines often diverge and the localization systems malfunction as detected features are not consistent with the precomputed 3D model. In this work, we present an approach to automatically detect dynamic object instances to improve the robustness of vision-based localization and mapping in crowded environments. By training a convolutional neural network model with a combination of synthetic and real-world data, dynamic object instance masks are learned in a semi-supervised way. The real-world data can be collected with a standard camera and requires minimal further post-processing. Our experiments show that a wide range of dynamic objects can be reliably detected using the presented method. Promising performance is demonstrated on our own and also publicly available datasets, which also shows the generalization capabilities of this approach.Comment: 4 pages, submitted to the IROS 2018 Workshop "From Freezing to Jostling Robots: Current Challenges and New Paradigms for Safe Robot Navigation in Dense Crowds

Mining for Observables: A New Challenge in Numerical Relativity

One of the motivations behind numerical relativity is to provide gravitational wave signals of compact objects to observers using the new gravitational wave detectors. Yet, because of the complexities involved, no dependable signals of binary-black hole coalescences have been established. The work in this proceedings is motivated by how numerical relativity can be used today to predict robust features in gravitational wave signals of binary black-hole coalescence by making approximations to the full problem. To illustrate this, we present results from evolving a Klein-Gordon equation on a frozen background. The background is set by a sequence of initial data in which the binary is in quasi-equilibrium. We probe the data resulting from the evolution for the transition between the linear and non-linear regimes using oscillations of the black holes as our guide. This information is used to motivate a qualitative picture of the gravitational signal of a black-hole coalescence