4,177 research outputs found
Probably Unknown: Deep Inverse Sensor Modelling In Radar
Radar presents a promising alternative to lidar and vision in autonomous
vehicle applications, able to detect objects at long range under a variety of
weather conditions. However, distinguishing between occupied and free space
from raw radar power returns is challenging due to complex interactions between
sensor noise and occlusion.
To counter this we propose to learn an Inverse Sensor Model (ISM) converting
a raw radar scan to a grid map of occupancy probabilities using a deep neural
network. Our network is self-supervised using partial occupancy labels
generated by lidar, allowing a robot to learn about world occupancy from past
experience without human supervision. We evaluate our approach on five hours of
data recorded in a dynamic urban environment. By accounting for the scene
context of each grid cell our model is able to successfully segment the world
into occupied and free space, outperforming standard CFAR filtering approaches.
Additionally by incorporating heteroscedastic uncertainty into our model
formulation, we are able to quantify the variance in the uncertainty throughout
the sensor observation. Through this mechanism we are able to successfully
identify regions of space that are likely to be occluded.Comment: 6 full pages, 1 page of reference
Data association and occlusion handling for vision-based people tracking by mobile robots
This paper presents an approach for tracking multiple persons on a mobile robot with a combination of colour and thermal vision sensors, using several new techniques. First, an adaptive colour model is incorporated into the measurement model of the tracker. Second, a new approach for detecting occlusions is introduced, using a machine learning classifier for pairwise comparison of persons (classifying which one is in front of the other). Third, explicit occlusion handling is incorporated into the tracker. The paper presents a comprehensive, quantitative evaluation of the whole system and its different components using several real world data sets
Reasoning About Liquids via Closed-Loop Simulation
Simulators are powerful tools for reasoning about a robot's interactions with
its environment. However, when simulations diverge from reality, that reasoning
becomes less useful. In this paper, we show how to close the loop between
liquid simulation and real-time perception. We use observations of liquids to
correct errors when tracking the liquid's state in a simulator. Our results
show that closed-loop simulation is an effective way to prevent large
divergence between the simulated and real liquid states. As a direct
consequence of this, our method can enable reasoning about liquids that would
otherwise be infeasible due to large divergences, such as reasoning about
occluded liquid.Comment: Robotics: Science & Systems (RSS), July 12-16, 2017. Cambridge, MA,
US
End-to-End Tracking and Semantic Segmentation Using Recurrent Neural Networks
In this work we present a novel end-to-end framework for tracking and
classifying a robot's surroundings in complex, dynamic and only partially
observable real-world environments. The approach deploys a recurrent neural
network to filter an input stream of raw laser measurements in order to
directly infer object locations, along with their identity in both visible and
occluded areas. To achieve this we first train the network using unsupervised
Deep Tracking, a recently proposed theoretical framework for end-to-end space
occupancy prediction. We show that by learning to track on a large amount of
unsupervised data, the network creates a rich internal representation of its
environment which we in turn exploit through the principle of inductive
transfer of knowledge to perform the task of it's semantic classification. As a
result, we show that only a small amount of labelled data suffices to steer the
network towards mastering this additional task. Furthermore we propose a novel
recurrent neural network architecture specifically tailored to tracking and
semantic classification in real-world robotics applications. We demonstrate the
tracking and classification performance of the method on real-world data
collected at a busy road junction. Our evaluation shows that the proposed
end-to-end framework compares favourably to a state-of-the-art, model-free
tracking solution and that it outperforms a conventional one-shot training
scheme for semantic classification
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