160,178 research outputs found
Depth from Monocular Images using a Semi-Parallel Deep Neural Network (SPDNN) Hybrid Architecture
Deep neural networks are applied to a wide range of problems in recent years.
In this work, Convolutional Neural Network (CNN) is applied to the problem of
determining the depth from a single camera image (monocular depth). Eight
different networks are designed to perform depth estimation, each of them
suitable for a feature level. Networks with different pooling sizes determine
different feature levels. After designing a set of networks, these models may
be combined into a single network topology using graph optimization techniques.
This "Semi Parallel Deep Neural Network (SPDNN)" eliminates duplicated common
network layers, and can be further optimized by retraining to achieve an
improved model compared to the individual topologies. In this study, four SPDNN
models are trained and have been evaluated at 2 stages on the KITTI dataset.
The ground truth images in the first part of the experiment are provided by the
benchmark, and for the second part, the ground truth images are the depth map
results from applying a state-of-the-art stereo matching method. The results of
this evaluation demonstrate that using post-processing techniques to refine the
target of the network increases the accuracy of depth estimation on individual
mono images. The second evaluation shows that using segmentation data alongside
the original data as the input can improve the depth estimation results to a
point where performance is comparable with stereo depth estimation. The
computational time is also discussed in this study.Comment: 44 pages, 25 figure
Learning sparse representations of depth
This paper introduces a new method for learning and inferring sparse
representations of depth (disparity) maps. The proposed algorithm relaxes the
usual assumption of the stationary noise model in sparse coding. This enables
learning from data corrupted with spatially varying noise or uncertainty,
typically obtained by laser range scanners or structured light depth cameras.
Sparse representations are learned from the Middlebury database disparity maps
and then exploited in a two-layer graphical model for inferring depth from
stereo, by including a sparsity prior on the learned features. Since they
capture higher-order dependencies in the depth structure, these priors can
complement smoothness priors commonly used in depth inference based on Markov
Random Field (MRF) models. Inference on the proposed graph is achieved using an
alternating iterative optimization technique, where the first layer is solved
using an existing MRF-based stereo matching algorithm, then held fixed as the
second layer is solved using the proposed non-stationary sparse coding
algorithm. This leads to a general method for improving solutions of state of
the art MRF-based depth estimation algorithms. Our experimental results first
show that depth inference using learned representations leads to state of the
art denoising of depth maps obtained from laser range scanners and a time of
flight camera. Furthermore, we show that adding sparse priors improves the
results of two depth estimation methods: the classical graph cut algorithm by
Boykov et al. and the more recent algorithm of Woodford et al.Comment: 12 page
Increasing the Efficiency of 6-DoF Visual Localization Using Multi-Modal Sensory Data
Localization is a key requirement for mobile robot autonomy and human-robot
interaction. Vision-based localization is accurate and flexible, however, it
incurs a high computational burden which limits its application on many
resource-constrained platforms. In this paper, we address the problem of
performing real-time localization in large-scale 3D point cloud maps of
ever-growing size. While most systems using multi-modal information reduce
localization time by employing side-channel information in a coarse manner (eg.
WiFi for a rough prior position estimate), we propose to inter-weave the map
with rich sensory data. This multi-modal approach achieves two key goals
simultaneously. First, it enables us to harness additional sensory data to
localise against a map covering a vast area in real-time; and secondly, it also
allows us to roughly localise devices which are not equipped with a camera. The
key to our approach is a localization policy based on a sequential Monte Carlo
estimator. The localiser uses this policy to attempt point-matching only in
nodes where it is likely to succeed, significantly increasing the efficiency of
the localization process. The proposed multi-modal localization system is
evaluated extensively in a large museum building. The results show that our
multi-modal approach not only increases the localization accuracy but
significantly reduces computational time.Comment: Presented at IEEE-RAS International Conference on Humanoid Robots
(Humanoids) 201
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
Theoretically Efficient Parallel Graph Algorithms Can Be Fast and Scalable
There has been significant recent interest in parallel graph processing due
to the need to quickly analyze the large graphs available today. Many graph
codes have been designed for distributed memory or external memory. However,
today even the largest publicly-available real-world graph (the Hyperlink Web
graph with over 3.5 billion vertices and 128 billion edges) can fit in the
memory of a single commodity multicore server. Nevertheless, most experimental
work in the literature report results on much smaller graphs, and the ones for
the Hyperlink graph use distributed or external memory. Therefore, it is
natural to ask whether we can efficiently solve a broad class of graph problems
on this graph in memory.
This paper shows that theoretically-efficient parallel graph algorithms can
scale to the largest publicly-available graphs using a single machine with a
terabyte of RAM, processing them in minutes. We give implementations of
theoretically-efficient parallel algorithms for 20 important graph problems. We
also present the optimizations and techniques that we used in our
implementations, which were crucial in enabling us to process these large
graphs quickly. We show that the running times of our implementations
outperform existing state-of-the-art implementations on the largest real-world
graphs. For many of the problems that we consider, this is the first time they
have been solved on graphs at this scale. We have made the implementations
developed in this work publicly-available as the Graph-Based Benchmark Suite
(GBBS).Comment: This is the full version of the paper appearing in the ACM Symposium
on Parallelism in Algorithms and Architectures (SPAA), 201
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