4 research outputs found
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
Tracking and Following Algorithms of Mobile Robots for Service Activities in Dynamic Environments
By providing the capability of following a human target in an appropriate manner, the robot can assist people in various ways under different environments. One of the main difficulties when performing human tracking and following is the occlusion problem caused by static as well as dynamic obstacles. The aim of the paper is to tackle the occlusion problem by planning a robotic trajectory of maximizing target visibility and following the moving target. Initially, a laser range finder is used to detect the human target and then robustly track the target using the Kalman filter. Afterward, a human following algorithm based on a look-ahead algorithm, DWA*, is implemented to pursue the target while avoiding any static or dynamic obstacles. Fundamental experiments have been extensively tested to evaluate robot maneuvers and several field tests are conducted in more complex environments such as student cafeteria, computer center, and university library.</span