747 research outputs found
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
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
A Survey on Human-aware Robot Navigation
Intelligent systems are increasingly part of our everyday lives and have been
integrated seamlessly to the point where it is difficult to imagine a world
without them. Physical manifestations of those systems on the other hand, in
the form of embodied agents or robots, have so far been used only for specific
applications and are often limited to functional roles (e.g. in the industry,
entertainment and military fields). Given the current growth and innovation in
the research communities concerned with the topics of robot navigation,
human-robot-interaction and human activity recognition, it seems like this
might soon change. Robots are increasingly easy to obtain and use and the
acceptance of them in general is growing. However, the design of a socially
compliant robot that can function as a companion needs to take various areas of
research into account. This paper is concerned with the navigation aspect of a
socially-compliant robot and provides a survey of existing solutions for the
relevant areas of research as well as an outlook on possible future directions.Comment: Robotics and Autonomous Systems, 202
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
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