1,980 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)
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Learning monocular visual odometry with dense 3D mapping from dense 3D flow
This paper introduces a fully deep learning approach to monocular SLAM, which
can perform simultaneous localization using a neural network for learning
visual odometry (L-VO) and dense 3D mapping. Dense 2D flow and a depth image
are generated from monocular images by sub-networks, which are then used by a
3D flow associated layer in the L-VO network to generate dense 3D flow. Given
this 3D flow, the dual-stream L-VO network can then predict the 6DOF relative
pose and furthermore reconstruct the vehicle trajectory. In order to learn the
correlation between motion directions, the Bivariate Gaussian modelling is
employed in the loss function. The L-VO network achieves an overall performance
of 2.68% for average translational error and 0.0143 deg/m for average
rotational error on the KITTI odometry benchmark. Moreover, the learned depth
is fully leveraged to generate a dense 3D map. As a result, an entire visual
SLAM system, that is, learning monocular odometry combined with dense 3D
mapping, is achieved.Comment: International Conference on Intelligent Robots and Systems(IROS 2018
Sparse 3D Point-cloud Map Upsampling and Noise Removal as a vSLAM Post-processing Step: Experimental Evaluation
The monocular vision-based simultaneous localization and mapping (vSLAM) is
one of the most challenging problem in mobile robotics and computer vision. In
this work we study the post-processing techniques applied to sparse 3D
point-cloud maps, obtained by feature-based vSLAM algorithms. Map
post-processing is split into 2 major steps: 1) noise and outlier removal and
2) upsampling. We evaluate different combinations of known algorithms for
outlier removing and upsampling on datasets of real indoor and outdoor
environments and identify the most promising combination. We further use it to
convert a point-cloud map, obtained by the real UAV performing indoor flight to
3D voxel grid (octo-map) potentially suitable for path planning.Comment: 10 pages, 4 figures, camera-ready version of paper for "The 3rd
International Conference on Interactive Collaborative Robotics (ICR 2018)
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