1,465 research outputs found
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
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 modeling is employed in the loss function. The L-VO network achieves an overall performance of 2.68 % for average translational error and 0.0143°/m for average rotational error on the KITTI odometry benchmark. Moreover, the learned depth is 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
GANVO: Unsupervised Deep Monocular Visual Odometry and Depth Estimation with Generative Adversarial Networks
In the last decade, supervised deep learning approaches have been extensively
employed in visual odometry (VO) applications, which is not feasible in
environments where labelled data is not abundant. On the other hand,
unsupervised deep learning approaches for localization and mapping in unknown
environments from unlabelled data have received comparatively less attention in
VO research. In this study, we propose a generative unsupervised learning
framework that predicts 6-DoF pose camera motion and monocular depth map of the
scene from unlabelled RGB image sequences, using deep convolutional Generative
Adversarial Networks (GANs). We create a supervisory signal by warping view
sequences and assigning the re-projection minimization to the objective loss
function that is adopted in multi-view pose estimation and single-view depth
generation network. Detailed quantitative and qualitative evaluations of the
proposed framework on the KITTI and Cityscapes datasets show that the proposed
method outperforms both existing traditional and unsupervised deep VO methods
providing better results for both pose estimation and depth recovery.Comment: ICRA 2019 - accepte
Keyframe-based monocular SLAM: design, survey, and future directions
Extensive research in the field of monocular SLAM for the past fifteen years
has yielded workable systems that found their way into various applications in
robotics and augmented reality. Although filter-based monocular SLAM systems
were common at some time, the more efficient keyframe-based solutions are
becoming the de facto methodology for building a monocular SLAM system. The
objective of this paper is threefold: first, the paper serves as a guideline
for people seeking to design their own monocular SLAM according to specific
environmental constraints. Second, it presents a survey that covers the various
keyframe-based monocular SLAM systems in the literature, detailing the
components of their implementation, and critically assessing the specific
strategies made in each proposed solution. Third, the paper provides insight
into the direction of future research in this field, to address the major
limitations still facing monocular SLAM; namely, in the issues of illumination
changes, initialization, highly dynamic motion, poorly textured scenes,
repetitive textures, map maintenance, and failure recovery
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