13,439 research outputs found
Autonomous Robot Navigation with Rich Information Mapping in Nuclear Storage Environments
This paper presents our approach to develop a method for an unmanned ground
vehicle (UGV) to perform inspection tasks in nuclear environments using rich
information maps. To reduce inspectors' exposure to elevated radiation levels,
an autonomous navigation framework for the UGV has been developed to perform
routine inspections such as counting containers, recording their ID tags and
performing gamma measurements on some of them. In order to achieve autonomy, a
rich information map is generated which includes not only the 2D global cost
map consisting of obstacle locations for path planning, but also the location
and orientation information for the objects of interest from the inspector's
perspective. The UGV's autonomy framework utilizes this information to
prioritize locations to navigate to perform the inspections. In this paper, we
present our method of generating this rich information map, originally
developed to meet the requirements of the International Atomic Energy Agency
(IAEA) Robotics Challenge. We demonstrate the performance of our method in a
simulated testbed environment containing uranium hexafluoride (UF6) storage
container mock ups
Interaction-aware Kalman Neural Networks for Trajectory Prediction
Forecasting the motion of surrounding obstacles (vehicles, bicycles,
pedestrians and etc.) benefits the on-road motion planning for intelligent and
autonomous vehicles. Complex scenes always yield great challenges in modeling
the patterns of surrounding traffic. For example, one main challenge comes from
the intractable interaction effects in a complex traffic system. In this paper,
we propose a multi-layer architecture Interaction-aware Kalman Neural Networks
(IaKNN) which involves an interaction layer for resolving high-dimensional
traffic environmental observations as interaction-aware accelerations, a motion
layer for transforming the accelerations to interaction aware trajectories, and
a filter layer for estimating future trajectories with a Kalman filter network.
Attributed to the multiple traffic data sources, our end-to-end trainable
approach technically fuses dynamic and interaction-aware trajectories boosting
the prediction performance. Experiments on the NGSIM dataset demonstrate that
IaKNN outperforms the state-of-the-art methods in terms of effectiveness for
traffic trajectory prediction.Comment: 8 pages, 4 figures, Accepted for IEEE Intelligent Vehicles Symposium
(IV) 202
Distributionally Robust Semi-Supervised Learning for People-Centric Sensing
Semi-supervised learning is crucial for alleviating labelling burdens in
people-centric sensing. However, human-generated data inherently suffer from
distribution shift in semi-supervised learning due to the diverse biological
conditions and behavior patterns of humans. To address this problem, we propose
a generic distributionally robust model for semi-supervised learning on
distributionally shifted data. Considering both the discrepancy and the
consistency between the labeled data and the unlabeled data, we learn the
latent features that reduce person-specific discrepancy and preserve
task-specific consistency. We evaluate our model in a variety of people-centric
recognition tasks on real-world datasets, including intention recognition,
activity recognition, muscular movement recognition and gesture recognition.
The experiment results demonstrate that the proposed model outperforms the
state-of-the-art methods.Comment: 8 pages, accepted by AAAI201
- …