19,579 research outputs found
Dynamic Body VSLAM with Semantic Constraints
Image based reconstruction of urban environments is a challenging problem
that deals with optimization of large number of variables, and has several
sources of errors like the presence of dynamic objects. Since most large scale
approaches make the assumption of observing static scenes, dynamic objects are
relegated to the noise modeling section of such systems. This is an approach of
convenience since the RANSAC based framework used to compute most multiview
geometric quantities for static scenes naturally confine dynamic objects to the
class of outlier measurements. However, reconstructing dynamic objects along
with the static environment helps us get a complete picture of an urban
environment. Such understanding can then be used for important robotic tasks
like path planning for autonomous navigation, obstacle tracking and avoidance,
and other areas. In this paper, we propose a system for robust SLAM that works
in both static and dynamic environments. To overcome the challenge of dynamic
objects in the scene, we propose a new model to incorporate semantic
constraints into the reconstruction algorithm. While some of these constraints
are based on multi-layered dense CRFs trained over appearance as well as motion
cues, other proposed constraints can be expressed as additional terms in the
bundle adjustment optimization process that does iterative refinement of 3D
structure and camera / object motion trajectories. We show results on the
challenging KITTI urban dataset for accuracy of motion segmentation and
reconstruction of the trajectory and shape of moving objects relative to ground
truth. We are able to show average relative error reduction by a significant
amount for moving object trajectory reconstruction relative to state-of-the-art
methods like VISO 2, as well as standard bundle adjustment algorithms
Learning Rank Reduced Interpolation with Principal Component Analysis
In computer vision most iterative optimization algorithms, both sparse and
dense, rely on a coarse and reliable dense initialization to bootstrap their
optimization procedure. For example, dense optical flow algorithms profit
massively in speed and robustness if they are initialized well in the basin of
convergence of the used loss function. The same holds true for methods as
sparse feature tracking when initial flow or depth information for new features
at arbitrary positions is needed. This makes it extremely important to have
techniques at hand that allow to obtain from only very few available
measurements a dense but still approximative sketch of a desired 2D structure
(e.g. depth maps, optical flow, disparity maps, etc.). The 2D map is regarded
as sample from a 2D random process. The method presented here exploits the
complete information given by the principal component analysis (PCA) of that
process, the principal basis and its prior distribution. The method is able to
determine a dense reconstruction from sparse measurement. When facing
situations with only very sparse measurements, typically the number of
principal components is further reduced which results in a loss of
expressiveness of the basis. We overcome this problem and inject prior
knowledge in a maximum a posterior (MAP) approach. We test our approach on the
KITTI and the virtual KITTI datasets and focus on the interpolation of depth
maps for driving scenes. The evaluation of the results show good agreement to
the ground truth and are clearly better than results of interpolation by the
nearest neighbor method which disregards statistical information.Comment: Accepted at Intelligent Vehicles Symposium (IV), Los Angeles, USA,
June 201
Scene Coordinate Regression with Angle-Based Reprojection Loss for Camera Relocalization
Image-based camera relocalization is an important problem in computer vision
and robotics. Recent works utilize convolutional neural networks (CNNs) to
regress for pixels in a query image their corresponding 3D world coordinates in
the scene. The final pose is then solved via a RANSAC-based optimization scheme
using the predicted coordinates. Usually, the CNN is trained with ground truth
scene coordinates, but it has also been shown that the network can discover 3D
scene geometry automatically by minimizing single-view reprojection loss.
However, due to the deficiencies of the reprojection loss, the network needs to
be carefully initialized. In this paper, we present a new angle-based
reprojection loss, which resolves the issues of the original reprojection loss.
With this new loss function, the network can be trained without careful
initialization, and the system achieves more accurate results. The new loss
also enables us to utilize available multi-view constraints, which further
improve performance.Comment: ECCV 2018 Workshop (Geometry Meets Deep Learning
Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos
Learning to predict scene depth from RGB inputs is a challenging task both
for indoor and outdoor robot navigation. In this work we address unsupervised
learning of scene depth and robot ego-motion where supervision is provided by
monocular videos, as cameras are the cheapest, least restrictive and most
ubiquitous sensor for robotics.
Previous work in unsupervised image-to-depth learning has established strong
baselines in the domain. We propose a novel approach which produces higher
quality results, is able to model moving objects and is shown to transfer
across data domains, e.g. from outdoors to indoor scenes. The main idea is to
introduce geometric structure in the learning process, by modeling the scene
and the individual objects; camera ego-motion and object motions are learned
from monocular videos as input. Furthermore an online refinement method is
introduced to adapt learning on the fly to unknown domains.
The proposed approach outperforms all state-of-the-art approaches, including
those that handle motion e.g. through learned flow. Our results are comparable
in quality to the ones which used stereo as supervision and significantly
improve depth prediction on scenes and datasets which contain a lot of object
motion. The approach is of practical relevance, as it allows transfer across
environments, by transferring models trained on data collected for robot
navigation in urban scenes to indoor navigation settings. The code associated
with this paper can be found at https://sites.google.com/view/struct2depth.Comment: Thirty-Third AAAI Conference on Artificial Intelligence (AAAI'19
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