663 research outputs found
Efficient Constellation-Based Map-Merging for Semantic SLAM
Data association in SLAM is fundamentally challenging, and handling ambiguity
well is crucial to achieve robust operation in real-world environments. When
ambiguous measurements arise, conservatism often mandates that the measurement
is discarded or a new landmark is initialized rather than risking an incorrect
association. To address the inevitable `duplicate' landmarks that arise, we
present an efficient map-merging framework to detect duplicate constellations
of landmarks, providing a high-confidence loop-closure mechanism well-suited
for object-level SLAM. This approach uses an incrementally-computable
approximation of landmark uncertainty that only depends on local information in
the SLAM graph, avoiding expensive recovery of the full system covariance
matrix. This enables a search based on geometric consistency (GC) (rather than
full joint compatibility (JC)) that inexpensively reduces the search space to a
handful of `best' hypotheses. Furthermore, we reformulate the commonly-used
interpretation tree to allow for more efficient integration of clique-based
pairwise compatibility, accelerating the branch-and-bound max-cardinality
search. Our method is demonstrated to match the performance of full JC methods
at significantly-reduced computational cost, facilitating robust object-based
loop-closure over large SLAM problems.Comment: Accepted to IEEE International Conference on Robotics and Automation
(ICRA) 201
An approach to clustering biological phenotypes /
Recently emerging approaches to high-throughput phenotyping have become important tools in unraveling the biological basis of agronomically and medically important phenotypes. These experiments produce very large sets of either low or high-dimensional data. Finding clusters in the entire space of high-dimensional data (HDD) is a challenging task, because the relative distances between any two objects converge to zero with increasing dimensionality. Additionally, real data may not be mathematically well behaved. Finally, many clusters are expected on biological grounds to be "natural" -- that is, to have irregular, overlapping boundaries in different subsets of the dimensions. More precisely, the natural clusters of the data could differ in shape, size, density, and dimensionality; and they might not be disjoint. In principle, clustering such data could be done by dimension reduction methods. However, these methods convert many dimensions to a smaller set of dimensions that make the clustering results difficult to interpret and may also lead to a significant loss of information. Another possible approach is to find subspaces (subsets of dimensions) in the entire data space of the HDD. However, the existing subspace methods don't discover natural clusters. Therefore, in this dissertation I propose a novel data preprocessing method, demonstrating that a group of phenotypes are interdependent, and propose a novel density-based subspace clustering algorithm for high-dimensional data, called Dynamic Locally Density Adaptive Scalable Subspace Clustering (DynaDASC). This algorithm is relatively locally density adaptive, scalable, dynamic, and nonmetric in nature, and discovers natural clusters.Dr. Toni Kazic, Dissertation Supervisor.|Includes vita.Includes bibliographical references (pages 62-73)
Representation Learning: A Review and New Perspectives
The success of machine learning algorithms generally depends on data
representation, and we hypothesize that this is because different
representations can entangle and hide more or less the different explanatory
factors of variation behind the data. Although specific domain knowledge can be
used to help design representations, learning with generic priors can also be
used, and the quest for AI is motivating the design of more powerful
representation-learning algorithms implementing such priors. This paper reviews
recent work in the area of unsupervised feature learning and deep learning,
covering advances in probabilistic models, auto-encoders, manifold learning,
and deep networks. This motivates longer-term unanswered questions about the
appropriate objectives for learning good representations, for computing
representations (i.e., inference), and the geometrical connections between
representation learning, density estimation and manifold learning
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Radar-based localization and mapping for large-scale environments and adverse weather conditions
In mobile robotics, localization and mapping is one of the fundamental capabilities
towards autonomy. Navigating autonomously in large-scale, unstructured, extreme
and dynamical environments is particularly challenging due to the high variations in
the scene. To deliver a robotic system that can operate 24/7 in outdoor environment,
we need to design a state estimation system that is robust in all weather conditions.
In this thesis, we propose, implement and validate three systems to tackle the
problem of long-term localization and mapping. We focus on using radar-only platform to realize the SLAM and localization systems in probabilistic manners. We
first introduce a radar-based SLAM system that can operate in city-scale environment. Second, we present an improved version of the radar-based SLAM system
with enhanced odometry estimation capability and extensive experiments on extreme weather conditions, proving that our proposed radar SLAM solution is viable
in all weather conditions. We also demonstrate the superiority of radar-based SLAM
system compared to LiDAR and vision based system in snowy and low light conditions respectively. Finally, we show how to combine online public maps and radar
sensor to achieve accurate localization even we do not have a prior sensor map. We
show that our proposed localization system can generalize to different scenarios and
we validate it across three datasets collected in three different continents
Data Mining Using the Crossing Minimization Paradigm
Our ability and capacity to generate, record and store multi-dimensional, apparently
unstructured data is increasing rapidly, while the cost of data storage is going down. The data recorded is not perfect, as noise gets introduced in it from different sources. Some of the basic forms of noise are incorrect recording of values and missing values. The formal study of discovering useful hidden information in the data is called Data Mining.
Because of the size, and complexity of the problem, practical data mining problems are
best attempted using automatic means.
Data Mining can be categorized into two types i.e. supervised learning or classification and unsupervised learning or clustering. Clustering only the records in a database (or data matrix) gives a global view of the data and is called one-way clustering. For a detailed analysis or a local view, biclustering or co-clustering or two-way clustering is required involving the simultaneous clustering of the records and the attributes.
In this dissertation, a novel fast and white noise tolerant data mining solution is
proposed based on the Crossing Minimization (CM) paradigm; the solution works for
one-way as well as two-way clustering for discovering overlapping biclusters. For
decades the CM paradigm has traditionally been used for graph drawing and VLSI
(Very Large Scale Integration) circuit design for reducing wire length and congestion. The utility of the proposed technique is demonstrated by comparing it with other biclustering techniques using simulated noisy, as well as real data from Agriculture, Biology and other domains.
Two other interesting and hard problems also addressed in this dissertation are (i) the
Minimum Attribute Subset Selection (MASS) problem and (ii) Bandwidth
Minimization (BWM) problem of sparse matrices. The proposed CM technique is
demonstrated to provide very convincing results while attempting to solve the said
problems using real public domain data.
Pakistan is the fourth largest supplier of cotton in the world. An apparent anomaly has
been observed during 1989-97 between cotton yield and pesticide consumption in
Pakistan showing unexpected periods of negative correlation. By applying the
indigenous CM technique for one-way clustering to real Agro-Met data (2001-2002), a possible explanation of the anomaly has been presented in this thesis
Energy-efficient information inference in wireless sensor networks based on graphical modeling
This dissertation proposes a systematic approach, based on a probabilistic graphical model, to infer missing observations in wireless sensor networks (WSNs) for sustaining environmental monitoring. This enables us to effectively address two critical challenges in WSNs: (1) energy-efficient data gathering through planned communication disruptions resulting from energy-saving sleep cycles, and (2) sensor-node failure tolerance in harsh environments. In our approach, we develop a pairwise Markov Random Field (MRF) to model the spatial correlations in a sensor network. Our MRF model is first constructed through automatic learning from historical sensed data, by using Iterative Proportional Fitting (IPF). When the MRF model is constructed, Loopy Belief Propagation (LBP) is then employed to perform information inference to estimate the missing data given incomplete network observations. The proposed approach is then improved in terms of energy-efficiency and robustness from three aspects: model building, inference and parameter learning. The model and methods are empirically evaluated using multiple real-world sensor network data sets. The results demonstrate the merits of our proposed approaches
Advances in Simultaneous Localization and Mapping in Confined Underwater Environments Using Sonar and Optical Imaging.
This thesis reports on the incorporation of surface information into a probabilistic simultaneous localization and mapping (SLAM) framework used on an autonomous underwater vehicle (AUV) designed for underwater inspection. AUVs operating in cluttered underwater environments, such as ship hulls or dams, are commonly equipped with Doppler-based sensors, which---in addition to navigation---provide a sparse representation of the environment in the form of a three-dimensional (3D) point cloud. The goal of this thesis is to develop perceptual algorithms that take full advantage of these sparse observations for correcting navigational drift and building a model of the environment. In particular, we focus on three objectives. First, we introduce a novel representation of this 3D point cloud as collections of planar features arranged in a factor graph. This factor graph representation probabalistically infers the spatial arrangement of each planar segment and can effectively model smooth surfaces (such as a ship hull). Second, we show how this technique can produce 3D models that serve as input to our pipeline that produces the first-ever 3D photomosaics using a two-dimensional (2D) imaging sonar. Finally, we propose a model-assisted bundle adjustment (BA) framework that allows for robust registration between surfaces observed from a Doppler sensor and visual features detected from optical images. Throughout this thesis, we show methods that produce 3D photomosaics using a combination of triangular meshes (derived from our SLAM framework or given a-priori), optical images, and sonar images. Overall, the contributions of this thesis greatly increase the accuracy, reliability, and utility of in-water ship hull inspection with AUVs despite the challenges they face in underwater environments.
We provide results using the Hovering Autonomous Underwater Vehicle (HAUV) for autonomous ship hull inspection, which serves as the primary testbed for the algorithms presented in this thesis. The sensor payload of the HAUV consists primarily of: a Doppler velocity log (DVL) for underwater navigation and ranging, monocular and stereo cameras, and---for some applications---an imaging sonar.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120750/1/paulozog_1.pd
- …