6,176 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
Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions
To plan safe trajectories in urban environments, autonomous vehicles must be
able to quickly assess the future intentions of dynamic agents. Pedestrians are
particularly challenging to model, as their motion patterns are often uncertain
and/or unknown a priori. This paper presents a novel changepoint detection and
clustering algorithm that, when coupled with offline unsupervised learning of a
Gaussian process mixture model (DPGP), enables quick detection of changes in
intent and online learning of motion patterns not seen in prior training data.
The resulting long-term movement predictions demonstrate improved accuracy
relative to offline learning alone, in terms of both intent and trajectory
prediction. By embedding these predictions within a chance-constrained motion
planner, trajectories which are probabilistically safe to pedestrian motions
can be identified in real-time. Hardware experiments demonstrate that this
approach can accurately predict pedestrian motion patterns from onboard
sensor/perception data and facilitate robust navigation within a dynamic
environment.Comment: Submitted to 2014 International Workshop on the Algorithmic
Foundations of Robotic
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
Change of Scenery: Unsupervised LiDAR Change Detection for Mobile Robots
This paper presents a fully unsupervised deep change detection approach for
mobile robots with 3D LiDAR. In unstructured environments, it is infeasible to
define a closed set of semantic classes. Instead, semantic segmentation is
reformulated as binary change detection. We develop a neural network,
RangeNetCD, that uses an existing point-cloud map and a live LiDAR scan to
detect scene changes with respect to the map. Using a novel loss function,
existing point-cloud semantic segmentation networks can be trained to perform
change detection without any labels or assumptions about local semantics. We
demonstrate the performance of this approach on data from challenging terrains;
mean intersection over union (mIoU) scores range between 67.4% and 82.2%
depending on the amount of environmental structure. This outperforms the
geometric baseline used in all experiments. The neural network runs faster than
10Hz and is integrated into a robot's autonomy stack to allow safe navigation
around obstacles that intersect the planned path. In addition, a novel method
for the rapid automated acquisition of per-point ground-truth labels is
described. Covering changed parts of the scene with retroreflective materials
and applying a threshold filter to the intensity channel of the LiDAR allows
for quantitative evaluation of the change detector.Comment: 7 pages (6 content, 1 references). 7 figures, submitted to the 2024
IEEE International Conference on Robotics and Automation (ICRA
- …