1,156 research outputs found
Robust Photogeometric Localization over Time for Map-Centric Loop Closure
Map-centric SLAM is emerging as an alternative of conventional graph-based
SLAM for its accuracy and efficiency in long-term mapping problems. However, in
map-centric SLAM, the process of loop closure differs from that of conventional
SLAM and the result of incorrect loop closure is more destructive and is not
reversible. In this paper, we present a tightly coupled photogeometric metric
localization for the loop closure problem in map-centric SLAM. In particular,
our method combines complementary constraints from LiDAR and camera sensors,
and validates loop closure candidates with sequential observations. The
proposed method provides a visual evidence-based outlier rejection where
failures caused by either place recognition or localization outliers can be
effectively removed. We demonstrate the proposed method is not only more
accurate than the conventional global ICP methods but is also robust to
incorrect initial pose guesses.Comment: To Appear in IEEE ROBOTICS AND AUTOMATION LETTERS, ACCEPTED JANUARY
201
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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