1,707 research outputs found
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
A multi-viewpoint feature-based re-identification system driven by skeleton keypoints
Thanks to the increasing popularity of 3D sensors, robotic vision has experienced huge improvements in a wide range of applications and systems in the last years. Besides the many benefits, this migration caused some incompatibilities with those systems that cannot be based on range sensors, like intelligent video surveillance systems, since the two kinds of sensor data lead to different representations of people and objects. This work goes in the direction of bridging the gap, and presents a novel re-identification system that takes advantage of multiple video flows in order to enhance the performance of a skeletal tracking algorithm, which is in turn exploited for driving the re-identification. A new, geometry-based method for joining together the detections provided by the skeletal tracker from multiple video flows is introduced, which is capable of dealing with many people in the scene, coping with the errors introduced in each view by the skeletal tracker. Such method has a high degree of generality, and can be applied to any kind of body pose estimation algorithm. The system was tested on a public dataset for video surveillance applications, demonstrating the improvements achieved by the multi-viewpoint approach in the accuracy of both body pose estimation and re-identification. The proposed approach was also compared with a skeletal tracking system working on 3D data: the comparison assessed the good performance level of the multi-viewpoint approach. This means that the lack of the rich information provided by 3D sensors can be compensated by the availability of more than one viewpoint
A Survey on Imitation Learning Techniques for End-to-End Autonomous Vehicles
Funding Agency: 10.13039/100016335-Jaguar Land Rover 10.13039/501100000266-U.K. Engineering and Physical Sciences Research Council (EPSRC) (Grant Number: EP/N01300X/1) jointly funded Towards Autonomy: Smart and Connected Control (TASCC) ProgramPeer reviewedPostprin
Robust Deep Multi-Modal Sensor Fusion using Fusion Weight Regularization and Target Learning
Sensor fusion has wide applications in many domains including health care and
autonomous systems. While the advent of deep learning has enabled promising
multi-modal fusion of high-level features and end-to-end sensor fusion
solutions, existing deep learning based sensor fusion techniques including deep
gating architectures are not always resilient, leading to the issue of fusion
weight inconsistency. We propose deep multi-modal sensor fusion architectures
with enhanced robustness particularly under the presence of sensor failures. At
the core of our gating architectures are fusion weight regularization and
fusion target learning operating on auxiliary unimodal sensing networks
appended to the main fusion model. The proposed regularized gating
architectures outperform the existing deep learning architectures with and
without gating under both clean and corrupted sensory inputs resulted from
sensor failures. The demonstrated improvements are particularly pronounced when
one or more multiple sensory modalities are corrupted.Comment: 8 page
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