1,911 research outputs found
New optimization techniques for point feature and general curve feature based SLAM
University of Technology, Sydney. Faculty of Engineering and Information Technology.This doctoral thesis deals with the feature based Simultaneous Localization and Mapping
(SLAM) problem. SLAM as defined in this thesis is the process of concurrently building
up a map of the environment and using this map to obtain improved estimates of the
location of the robot. In feature based SLAM, the robot relies on its ability to extract
useful navigation information from the data returned by its sensors. The robot typically
starts at an unknown location without priori knowledge of feature locations. From relative
observations of features and relative pose measurements, estimates of entire robot trajectory
and feature locations can be derived. Thus, the solution to SLAM problem enables
an autonomous vehicle navigates in a unknown environment autonomously. The advantage
of eliminating the need for artificial infrastructures or a priori topological knowledge
of the environment makes SLAM problem one of the hot research topics in the robotics
literature. Solution to the SLAM problem would be of inestimable value in a range of
applications such as exploration, surveillance, transportation, mining etc.
The critical problems for feature based SLAM implementations are as follows: 1) Because
SLAM problems are high dimensional, nonlinear and non-convex, when solving
SLAM problems, robust optimization techniques are required. 2) When the environment
is complex and unstructured, appropriate parametrization method is required to represent
environments with minimum information loss. 3) As robot navigates in the environment,
the information acquired by the onboard sensor increases. It is essential to develop
computationally tractable SLAM algorithms especially for general curve features.
This thesis presents the following contributions to feature based SLAM. First, a convex
optimization based approach for point feature SLAM problems is developed. Using the
proposed method, a unique solution can be obtained without any initial state estimates.
It will be shown that, the unique SDP solution obtained from the proposed method is very
close to the true solution to the SLAM problem. Second, a general curve feature based
SLAM formulation is presented. Instead of scattered points, in this formulation, the
environment is represented by a number of continuous curves. Using the new formulation, all
the available information from the sensor is utilized in the optimization process. Third,
method for converting curve feature to point feature is presented. Using the conversion
method, the curve feature SLAM problem can be transferred to point feature SLAM problem
and can be solved by the convex optimization based approach
Modeling Perceptual Aliasing in SLAM via Discrete-Continuous Graphical Models
Perceptual aliasing is one of the main causes of failure for Simultaneous
Localization and Mapping (SLAM) systems operating in the wild. Perceptual
aliasing is the phenomenon where different places generate a similar visual
(or, in general, perceptual) footprint. This causes spurious measurements to be
fed to the SLAM estimator, which typically results in incorrect localization
and mapping results. The problem is exacerbated by the fact that those outliers
are highly correlated, in the sense that perceptual aliasing creates a large
number of mutually-consistent outliers. Another issue stems from the fact that
most state-of-the-art techniques rely on a given trajectory guess (e.g., from
odometry) to discern between inliers and outliers and this makes the resulting
pipeline brittle, since the accumulation of error may result in incorrect
choices and recovery from failures is far from trivial. This work provides a
unified framework to model perceptual aliasing in SLAM and provides practical
algorithms that can cope with outliers without relying on any initial guess. We
present two main contributions. The first is a Discrete-Continuous Graphical
Model (DC-GM) for SLAM: the continuous portion of the DC-GM captures the
standard SLAM problem, while the discrete portion describes the selection of
the outliers and models their correlation. The second contribution is a
semidefinite relaxation to perform inference in the DC-GM that returns
estimates with provable sub-optimality guarantees. Experimental results on
standard benchmarking datasets show that the proposed technique compares
favorably with state-of-the-art methods while not relying on an initial guess
for optimization.Comment: 13 pages, 14 figures, 1 tabl
GSLAM: Initialization-robust Monocular Visual SLAM via Global Structure-from-Motion
Many monocular visual SLAM algorithms are derived from incremental
structure-from-motion (SfM) methods. This work proposes a novel monocular SLAM
method which integrates recent advances made in global SfM. In particular, we
present two main contributions to visual SLAM. First, we solve the visual
odometry problem by a novel rank-1 matrix factorization technique which is more
robust to the errors in map initialization. Second, we adopt a recent global
SfM method for the pose-graph optimization, which leads to a multi-stage linear
formulation and enables L1 optimization for better robustness to false loops.
The combination of these two approaches generates more robust reconstruction
and is significantly faster (4X) than recent state-of-the-art SLAM systems. We
also present a new dataset recorded with ground truth camera motion in a Vicon
motion capture room, and compare our method to prior systems on it and
established benchmark datasets.Comment: 3DV 2017 Project Page: https://frobelbest.github.io/gsla
Lagrangian Duality in 3D SLAM: Verification Techniques and Optimal Solutions
State-of-the-art techniques for simultaneous localization and mapping (SLAM)
employ iterative nonlinear optimization methods to compute an estimate for
robot poses. While these techniques often work well in practice, they do not
provide guarantees on the quality of the estimate. This paper shows that
Lagrangian duality is a powerful tool to assess the quality of a given
candidate solution. Our contribution is threefold. First, we discuss a revised
formulation of the SLAM inference problem. We show that this formulation is
probabilistically grounded and has the advantage of leading to an optimization
problem with quadratic objective. The second contribution is the derivation of
the corresponding Lagrangian dual problem. The SLAM dual problem is a (convex)
semidefinite program, which can be solved reliably and globally by
off-the-shelf solvers. The third contribution is to discuss the relation
between the original SLAM problem and its dual. We show that from the dual
problem, one can evaluate the quality (i.e., the suboptimality gap) of a
candidate SLAM solution, and ultimately provide a certificate of optimality.
Moreover, when the duality gap is zero, one can compute a guaranteed optimal
SLAM solution from the dual problem, circumventing non-convex optimization. We
present extensive (real and simulated) experiments supporting our claims and
discuss practical relevance and open problems.Comment: 10 pages, 4 figure
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
Learning to Navigate the Energy Landscape
In this paper, we present a novel and efficient architecture for addressing
computer vision problems that use `Analysis by Synthesis'. Analysis by
synthesis involves the minimization of the reconstruction error which is
typically a non-convex function of the latent target variables.
State-of-the-art methods adopt a hybrid scheme where discriminatively trained
predictors like Random Forests or Convolutional Neural Networks are used to
initialize local search algorithms. While these methods have been shown to
produce promising results, they often get stuck in local optima. Our method
goes beyond the conventional hybrid architecture by not only proposing multiple
accurate initial solutions but by also defining a navigational structure over
the solution space that can be used for extremely efficient gradient-free local
search. We demonstrate the efficacy of our approach on the challenging problem
of RGB Camera Relocalization. To make the RGB camera relocalization problem
particularly challenging, we introduce a new dataset of 3D environments which
are significantly larger than those found in other publicly-available datasets.
Our experiments reveal that the proposed method is able to achieve
state-of-the-art camera relocalization results. We also demonstrate the
generalizability of our approach on Hand Pose Estimation and Image Retrieval
tasks
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