16,362 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
Active End-Effector Pose Selection for Tactile Object Recognition through Monte Carlo Tree Search
This paper considers the problem of active object recognition using touch
only. The focus is on adaptively selecting a sequence of wrist poses that
achieves accurate recognition by enclosure grasps. It seeks to minimize the
number of touches and maximize recognition confidence. The actions are
formulated as wrist poses relative to each other, making the algorithm
independent of absolute workspace coordinates. The optimal sequence is
approximated by Monte Carlo tree search. We demonstrate results in a physics
engine and on a real robot. In the physics engine, most object instances were
recognized in at most 16 grasps. On a real robot, our method recognized objects
in 2--9 grasps and outperformed a greedy baseline.Comment: Accepted to International Conference on Intelligent Robots and
Systems (IROS) 201
Active End-Effector Pose Selection for Tactile Object Recognition through Monte Carlo Tree Search
This paper considers the problem of active object recognition using touch
only. The focus is on adaptively selecting a sequence of wrist poses that
achieves accurate recognition by enclosure grasps. It seeks to minimize the
number of touches and maximize recognition confidence. The actions are
formulated as wrist poses relative to each other, making the algorithm
independent of absolute workspace coordinates. The optimal sequence is
approximated by Monte Carlo tree search. We demonstrate results in a physics
engine and on a real robot. In the physics engine, most object instances were
recognized in at most 16 grasps. On a real robot, our method recognized objects
in 2--9 grasps and outperformed a greedy baseline.Comment: Accepted to International Conference on Intelligent Robots and
Systems (IROS) 201
SegICP: Integrated Deep Semantic Segmentation and Pose Estimation
Recent robotic manipulation competitions have highlighted that sophisticated
robots still struggle to achieve fast and reliable perception of task-relevant
objects in complex, realistic scenarios. To improve these systems' perceptive
speed and robustness, we present SegICP, a novel integrated solution to object
recognition and pose estimation. SegICP couples convolutional neural networks
and multi-hypothesis point cloud registration to achieve both robust pixel-wise
semantic segmentation as well as accurate and real-time 6-DOF pose estimation
for relevant objects. Our architecture achieves 1cm position error and
<5^\circ$ angle error in real time without an initial seed. We evaluate and
benchmark SegICP against an annotated dataset generated by motion capture.Comment: IROS camera-read
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