8,260 research outputs found
Active SLAM for autonomous underwater exploration
Exploration of a complex underwater environment without an a priori map is beyond the state of the art for autonomous underwater vehicles (AUVs). Despite several efforts regarding simultaneous localization and mapping (SLAM) and view planning, there is no exploration framework, tailored to underwater vehicles, that faces exploration combining mapping, active localization, and view planning in a unified way. We propose an exploration framework, based on an active SLAM strategy, that combines three main elements: a view planner, an iterative closest point algorithm (ICP)-based pose-graph SLAM algorithm, and an action selection mechanism that makes use of the joint map and state entropy reduction. To demonstrate the benefits of the active SLAM strategy, several tests were conducted with the Girona 500 AUV, both in simulation and in the real world. The article shows how the proposed framework makes it possible to plan exploratory trajectories that keep the vehicle’s uncertainty bounded; thus, creating more consistent maps.Peer ReviewedPostprint (published version
Network Uncertainty Informed Semantic Feature Selection for Visual SLAM
In order to facilitate long-term localization using a visual simultaneous
localization and mapping (SLAM) algorithm, careful feature selection can help
ensure that reference points persist over long durations and the runtime and
storage complexity of the algorithm remain consistent. We present SIVO
(Semantically Informed Visual Odometry and Mapping), a novel
information-theoretic feature selection method for visual SLAM which
incorporates semantic segmentation and neural network uncertainty into the
feature selection pipeline. Our algorithm selects points which provide the
highest reduction in Shannon entropy between the entropy of the current state
and the joint entropy of the state, given the addition of the new feature with
the classification entropy of the feature from a Bayesian neural network. Each
selected feature significantly reduces the uncertainty of the vehicle state and
has been detected to be a static object (building, traffic sign, etc.)
repeatedly with a high confidence. This selection strategy generates a sparse
map which can facilitate long-term localization. The KITTI odometry dataset is
used to evaluate our method, and we also compare our results against ORB_SLAM2.
Overall, SIVO performs comparably to the baseline method while reducing the map
size by almost 70%.Comment: Published in: 2019 16th Conference on Computer and Robot Vision (CRV
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Motion Planning for Optimal Information Gathering in Opportunistic Navigation Systems
Motion planning for optimal information gathering in an opportunistic navigation (OpNav)
environment is considered. An OpNav environment can be thought of as a radio
frequency signal landscape within which a receiver locates itself in space and time by extracting
information from ambient signals of opportunity (SOPs). The receiver is assumed
to draw only pseudorange-type observations from the SOPs, and such observations are
fused through an estimator to produce an estimate of the receiver’s own states. Since
not all SOP states in the OpNav environment may be known a priori, the receiver must
estimate the unknown SOP states of interest simultaneously with its own states. In this
work, the following problem is studied. A receiver with no a priori knowledge about its
own states is dropped in an unknown, yet observable, OpNav environment. Assuming that
the receiver can prescribe its own trajectory, what motion planning strategy should the
receiver adopt in order to build a high-fidelity map of the OpNav signal landscape, while
simultaneously localizing itself within this map in space and time? To answer this question,
first, the minimum conditions under which the OpNav environment is fully observable are
established, and the need for receiver maneuvering to achieve full observability is highlighted.
Then, motivated by the fact that not all trajectories a receiver may take in the
environment are equally beneficial from an information gathering point of view, a strategy
for planning the motion of the receiver is proposed. The strategy is formulated in a
coupled estimation and optimal control framework of a gradually identified system, where
optimality is defined through various information-theoretic measures. Simulation results
are presented to illustrate the improvements gained from adopting the proposed strategy
over random and pre-defined receiver trajectories.Aerospace Engineering and Engineering Mechanic
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
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