56 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
Advancing Robot Autonomy for Long-Horizon Tasks
Autonomous robots have real-world applications in diverse fields, such as
mobile manipulation and environmental exploration, and many such tasks benefit
from a hands-off approach in terms of human user involvement over a long task
horizon. However, the level of autonomy achievable by a deployment is limited
in part by the problem definition or task specification required by the system.
Task specifications often require technical, low-level information that is
unintuitive to describe and may result in generic solutions, burdening the user
technically both before and after task completion. In this thesis, we aim to
advance task specification abstraction toward the goal of increasing robot
autonomy in real-world scenarios. We do so by tackling problems that address
several different angles of this goal. First, we develop a way for the
automatic discovery of optimal transition points between subtasks in the
context of constrained mobile manipulation, removing the need for the human to
hand-specify these in the task specification. We further propose a way to
automatically describe constraints on robot motion by using demonstrated data
as opposed to manually-defined constraints. Then, within the context of
environmental exploration, we propose a flexible task specification framework,
requiring just a set of quantiles of interest from the user that allows the
robot to directly suggest locations in the environment for the user to study.
We next systematically study the effect of including a robot team in the task
specification and show that multirobot teams have the ability to improve
performance under certain specification conditions, including enabling
inter-robot communication. Finally, we propose methods for a communication
protocol that autonomously selects useful but limited information to share with
the other robots.Comment: PhD dissertation. 160 page
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