78 research outputs found
Information-Driven Adaptive Structured-Light Scanners
Sensor planning and active sensing, long studied in robotics, adapt sensor parameters to maximize a utility function while constraining resource expenditures. Here we consider information gain as the utility function. While these concepts are often used to reason about 3D sensors, these are usually treated as a predefined, black-box, component. In this paper we show how the same principles can be used as part of the 3D sensor. We describe the relevant generative model for structured-light 3D scanning and show how adaptive pattern selection can maximize information gain in an open-loop-feedback manner. We then demonstrate how different choices of relevant variable sets (corresponding to the subproblems of locatization and mapping) lead to different criteria for pattern selection and can be computed in an online fashion. We show results for both subproblems with several pattern dictionary choices and demonstrate their usefulness for pose estimation and depth acquisition.United States. Office of Naval Research (Grant N00014-09-1-1051)United States. Army Research Office (Grant W911NF-11- 1-0391)United States. Office of Naval Research (Grant N00014- 11-1-0688
Multi-Abstractive Neural Controller: An Efficient Hierarchical Control Architecture for Interactive Driving
As learning-based methods make their way from perception systems to
planning/control stacks, robot control systems have started to enjoy the
benefits that data-driven methods provide. Because control systems directly
affect the motion of the robot, data-driven methods, especially black box
approaches, need to be used with caution considering aspects such as stability
and interpretability. In this paper, we describe a differentiable and
hierarchical control architecture. The proposed representation, called
\textit{multi-abstractive neural controller}, uses the input image to control
the transitions within a novel discrete behavior planner (referred to as the
visual automaton generative network, or \textit{vAGN}). The output of a vAGN
controls the parameters of a set of dynamic movement primitives which provides
the system controls. We train this neural controller with real-world driving
data via behavior cloning and show improved explainability, sample efficiency,
and similarity to human driving
Deep Context Maps: Agent Trajectory Prediction using Location-specific Latent Maps
In this paper, we propose a novel approach for agent motion prediction in
cluttered environments. One of the main challenges in predicting agent motion
is accounting for location and context-specific information. Our main
contribution is the concept of learning context maps to improve the prediction
task. Context maps are a set of location-specific latent maps that are trained
alongside the predictor. Thus, the proposed maps are capable of capturing
location context beyond visual context cues (e.g. usual average speeds and
typical trajectories) or predefined map primitives (such as lanes and stop
lines). We pose context map learning as a multi-task training problem and
describe our map model and its incorporation into a state-of-the-art trajectory
predictor. In extensive experiments, it is shown that use of learned maps can
significantly improve predictor accuracy. Furthermore, the performance can be
additionally boosted by providing partial knowledge of map semantics
A Mixture of Manhattan Frames: Beyond the Manhattan World
Objects and structures within man-made environments typically exhibit a high degree of organization in the form of orthogonal and parallel planes. Traditional approaches to scene representation exploit this phenomenon via the somewhat restrictive assumption that every plane is perpendicular to one of the axes of a single coordinate system. Known as the Manhattan-World model, this assumption is widely used in computer vision and robotics. The complexity of many real-world scenes, however, necessitates a more flexible model. We propose a novel probabilistic model that describes the world as a mixture of Manhattan frames: each frame defines a different orthogonal coordinate system. This results in a more expressive model that still exploits the orthogonality constraints. We propose an adaptive Markov-Chain Monte-Carlo sampling algorithm with Metropolis-Hastings split/merge moves that utilizes the geometry of the unit sphere. We demonstrate the versatility of our Mixture-of-Manhattan-Frames model by describing complex scenes using depth images of indoor scenes as well as aerial-LiDAR measurements of an urban center. Additionally, we show that the model lends itself to focal-length calibration of depth cameras and to plane segmentation.United States. Office of Naval Research. Multidisciplinary University Research Initiative (Award N00014-11-1-0688)United States. Defense Advanced Research Projects Agency (Award FA8650-11-1-7154)Technion, Israel Institute of Technology (MIT Postdoctoral Fellowship Program
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