1,731 research outputs found
Pop-up SLAM: Semantic Monocular Plane SLAM for Low-texture Environments
Existing simultaneous localization and mapping (SLAM) algorithms are not
robust in challenging low-texture environments because there are only few
salient features. The resulting sparse or semi-dense map also conveys little
information for motion planning. Though some work utilize plane or scene layout
for dense map regularization, they require decent state estimation from other
sources. In this paper, we propose real-time monocular plane SLAM to
demonstrate that scene understanding could improve both state estimation and
dense mapping especially in low-texture environments. The plane measurements
come from a pop-up 3D plane model applied to each single image. We also combine
planes with point based SLAM to improve robustness. On a public TUM dataset,
our algorithm generates a dense semantic 3D model with pixel depth error of 6.2
cm while existing SLAM algorithms fail. On a 60 m long dataset with loops, our
method creates a much better 3D model with state estimation error of 0.67%.Comment: International Conference on Intelligent Robots and Systems (IROS)
201
MOMA: Visual Mobile Marker Odometry
In this paper, we present a cooperative odometry scheme based on the
detection of mobile markers in line with the idea of cooperative positioning
for multiple robots [1]. To this end, we introduce a simple optimization scheme
that realizes visual mobile marker odometry via accurate fixed marker-based
camera positioning and analyse the characteristics of errors inherent to the
method compared to classical fixed marker-based navigation and visual odometry.
In addition, we provide a specific UAV-UGV configuration that allows for
continuous movements of the UAV without doing stops and a minimal
caterpillar-like configuration that works with one UGV alone. Finally, we
present a real-world implementation and evaluation for the proposed UAV-UGV
configuration
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