1,018 research outputs found
DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration
We present DeepICP - a novel end-to-end learning-based 3D point cloud
registration framework that achieves comparable registration accuracy to prior
state-of-the-art geometric methods. Different from other keypoint based methods
where a RANSAC procedure is usually needed, we implement the use of various
deep neural network structures to establish an end-to-end trainable network.
Our keypoint detector is trained through this end-to-end structure and enables
the system to avoid the inference of dynamic objects, leverages the help of
sufficiently salient features on stationary objects, and as a result, achieves
high robustness. Rather than searching the corresponding points among existing
points, the key contribution is that we innovatively generate them based on
learned matching probabilities among a group of candidates, which can boost the
registration accuracy. Our loss function incorporates both the local similarity
and the global geometric constraints to ensure all above network designs can
converge towards the right direction. We comprehensively validate the
effectiveness of our approach using both the KITTI dataset and the
Apollo-SouthBay dataset. Results demonstrate that our method achieves
comparable or better performance than the state-of-the-art geometry-based
methods. Detailed ablation and visualization analysis are included to further
illustrate the behavior and insights of our network. The low registration error
and high robustness of our method makes it attractive for substantial
applications relying on the point cloud registration task.Comment: 10 pages, 6 figures, 3 tables, typos corrected, experimental results
updated, accepted by ICCV 201
Learning to See the Wood for the Trees: Deep Laser Localization in Urban and Natural Environments on a CPU
Localization in challenging, natural environments such as forests or
woodlands is an important capability for many applications from guiding a robot
navigating along a forest trail to monitoring vegetation growth with handheld
sensors. In this work we explore laser-based localization in both urban and
natural environments, which is suitable for online applications. We propose a
deep learning approach capable of learning meaningful descriptors directly from
3D point clouds by comparing triplets (anchor, positive and negative examples).
The approach learns a feature space representation for a set of segmented point
clouds that are matched between a current and previous observations. Our
learning method is tailored towards loop closure detection resulting in a small
model which can be deployed using only a CPU. The proposed learning method
would allow the full pipeline to run on robots with limited computational
payload such as drones, quadrupeds or UGVs.Comment: Accepted for publication at RA-L/ICRA 2019. More info:
https://ori.ox.ac.uk/esm-localizatio
When Deep Learning Meets Data Alignment: A Review on Deep Registration Networks (DRNs)
Registration is the process that computes the transformation that aligns sets
of data. Commonly, a registration process can be divided into four main steps:
target selection, feature extraction, feature matching, and transform
computation for the alignment. The accuracy of the result depends on multiple
factors, the most significant are the quantity of input data, the presence of
noise, outliers and occlusions, the quality of the extracted features,
real-time requirements and the type of transformation, especially those ones
defined by multiple parameters, like non-rigid deformations.
Recent advancements in machine learning could be a turning point in these
issues, particularly with the development of deep learning (DL) techniques,
which are helping to improve multiple computer vision problems through an
abstract understanding of the input data. In this paper, a review of deep
learning-based registration methods is presented. We classify the different
papers proposing a framework extracted from the traditional registration
pipeline to analyse the new learning-based proposal strengths. Deep
Registration Networks (DRNs) try to solve the alignment task either replacing
part of the traditional pipeline with a network or fully solving the
registration problem. The main conclusions extracted are, on the one hand, 1)
learning-based registration techniques cannot always be clearly classified in
the traditional pipeline. 2) These approaches allow more complex inputs like
conceptual models as well as the traditional 3D datasets. 3) In spite of the
generality of learning, the current proposals are still ad hoc solutions.
Finally, 4) this is a young topic that still requires a large effort to reach
general solutions able to cope with the problems that affect traditional
approaches.Comment: Submitted to Pattern Recognitio
Semantic Visual Localization
Robust visual localization under a wide range of viewing conditions is a
fundamental problem in computer vision. Handling the difficult cases of this
problem is not only very challenging but also of high practical relevance,
e.g., in the context of life-long localization for augmented reality or
autonomous robots. In this paper, we propose a novel approach based on a joint
3D geometric and semantic understanding of the world, enabling it to succeed
under conditions where previous approaches failed. Our method leverages a novel
generative model for descriptor learning, trained on semantic scene completion
as an auxiliary task. The resulting 3D descriptors are robust to missing
observations by encoding high-level 3D geometric and semantic information.
Experiments on several challenging large-scale localization datasets
demonstrate reliable localization under extreme viewpoint, illumination, and
geometry changes
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