14,729 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
C-blox: A Scalable and Consistent TSDF-based Dense Mapping Approach
In many applications, maintaining a consistent dense map of the environment
is key to enabling robotic platforms to perform higher level decision making.
Several works have addressed the challenge of creating precise dense 3D maps
from visual sensors providing depth information. However, during operation over
longer missions, reconstructions can easily become inconsistent due to
accumulated camera tracking error and delayed loop closure. Without explicitly
addressing the problem of map consistency, recovery from such distortions tends
to be difficult. We present a novel system for dense 3D mapping which addresses
the challenge of building consistent maps while dealing with scalability.
Central to our approach is the representation of the environment as a
collection of overlapping TSDF subvolumes. These subvolumes are localized
through feature-based camera tracking and bundle adjustment. Our main
contribution is a pipeline for identifying stable regions in the map, and to
fuse the contributing subvolumes. This approach allows us to reduce map growth
while still maintaining consistency. We demonstrate the proposed system on a
publicly available dataset and simulation engine, and demonstrate the efficacy
of the proposed approach for building consistent and scalable maps. Finally we
demonstrate our approach running in real-time on-board a lightweight MAV.Comment: 8 pages, 5 figures, conferenc
Data-Efficient Decentralized Visual SLAM
Decentralized visual simultaneous localization and mapping (SLAM) is a
powerful tool for multi-robot applications in environments where absolute
positioning systems are not available. Being visual, it relies on cameras,
cheap, lightweight and versatile sensors, and being decentralized, it does not
rely on communication to a central ground station. In this work, we integrate
state-of-the-art decentralized SLAM components into a new, complete
decentralized visual SLAM system. To allow for data association and
co-optimization, existing decentralized visual SLAM systems regularly exchange
the full map data between all robots, incurring large data transfers at a
complexity that scales quadratically with the robot count. In contrast, our
method performs efficient data association in two stages: in the first stage a
compact full-image descriptor is deterministically sent to only one robot. In
the second stage, which is only executed if the first stage succeeded, the data
required for relative pose estimation is sent, again to only one robot. Thus,
data association scales linearly with the robot count and uses highly compact
place representations. For optimization, a state-of-the-art decentralized
pose-graph optimization method is used. It exchanges a minimum amount of data
which is linear with trajectory overlap. We characterize the resulting system
and identify bottlenecks in its components. The system is evaluated on publicly
available data and we provide open access to the code.Comment: 8 pages, submitted to ICRA 201
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