1,930 research outputs found
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
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
Master of Science
thesisGait analysis is an important tool for diagnosing a wide variety of disorders, with its increasingly accepted benefits culminating in the widespread adoption of motion analysis laboratories. A modern analysis laboratory consists of a multicamera marker tracking system for 3D reconstruction of kinematics and multiple high-fidelity load transducers to determine ground reaction force and enable inverse-dynamics for biomechanics. There is a need for an alternative motion analysis system which does not require a fixed laboratory setting and is lower in cost; freeing the motion capture from the laboratory and reducing the technology costs would enable long-term, home-based, natural monitoring of subjects. This thesis describes two contributions to the end goal of an inexpensive, mobile, insole-based motion analysis laboratory. First is the application of an inertialmeasurement-unit calibration routine and zero-velocity-update algorithm to improve position and orientation tracking. Second is the development, from basic sensor to prototype, of an insole capable of measuring 3 degree-of-freedom ground reaction force. These contributions represent a proof-of-concept that quantitative gait analysis, complete with dynamics, is possible with an insole-based system
Exploiting Structural Regularities and Beyond: Vision-based Localization and Mapping in Man-Made Environments
Image-based estimation of camera motion, known as visual odometry
(VO), plays a very important role in many robotic applications
such as control and navigation of unmanned mobile robots,
especially when no external navigation reference signal is
available. The core problem of VO is the estimation of the
camera’s ego-motion (i.e. tracking) either between successive
frames, namely relative pose estimation, or with respect to a
global map, namely absolute pose estimation. This thesis aims to
develop efficient, accurate and robust VO solutions by taking
advantage of structural regularities in man-made environments,
such as piece-wise planar structures, Manhattan World and more
generally, contours and edges. Furthermore, to handle challenging
scenarios that are beyond the limits of classical sensor based VO
solutions, we investigate a recently emerging sensor — the
event camera and study on event-based mapping — one of the key
problems in the event-based VO/SLAM. The main achievements are
summarized as follows.
First, we revisit an old topic on relative pose estimation:
accurately and robustly estimating the fundamental matrix given a
collection of independently estimated homograhies. Three
classical methods are reviewed and then we show a simple but
nontrivial two-step normalization
within the direct linear method that achieves similar performance
to the less attractive and more computationally intensive
hallucinated points based method.
Second, an efficient 3D rotation estimation algorithm for depth
cameras in piece-wise planar environments is presented. It shows
that by using surface normal vectors as an input, planar modes in
the corresponding density distribution function can be discovered
and continuously
tracked using efficient non-parametric estimation techniques. The
relative rotation can be estimated by registering entire bundles
of planar modes by using robust L1-norm minimization.
Third, an efficient alternative to the iterative closest point
algorithm for real-time tracking of modern depth cameras in
ManhattanWorlds is developed. We exploit the common orthogonal
structure of man-made environments in order to decouple the
estimation of the rotation and the three degrees of freedom of
the translation. The derived camera orientation is absolute and
thus free of long-term drift, which in turn benefits the accuracy
of the translation estimation as well.
Fourth, we look into a more general structural
regularity—edges. A real-time VO system that uses Canny edges
is proposed for RGB-D cameras. Two novel alternatives to
classical distance transforms are developed with great properties
that significantly improve the classical Euclidean distance field
based methods in terms of efficiency, accuracy and robustness.
Finally, to deal with challenging scenarios that go beyond what
standard RGB/RGB-D cameras can handle, we investigate the
recently emerging event camera and focus on the problem of 3D
reconstruction from data captured by a stereo event-camera rig
moving in a static
scene, such as in the context of stereo Simultaneous Localization
and Mapping
Camera localization using trajectories and maps
We propose a new Bayesian framework for automatically determining the position (location and orientation) of an uncalibrated camera using the observations of moving objects and a schematic map of the passable areas of the environment. Our approach takes advantage of static and dynamic information on the scene structures through prior probability distributions for object dynamics. The proposed approach restricts plausible positions where the sensor can be located while taking into account the inherent ambiguity of the given setting. The proposed framework samples from the posterior probability distribution for the camera position via data driven MCMC, guided by an initial geometric analysis that restricts the search space. A Kullback-Leibler divergence analysis is then used that yields the final camera position estimate, while explicitly isolating ambiguous settings. The proposed approach is evaluated in synthetic and real environments, showing its satisfactory performance in both ambiguous and unambiguous settings
Information and distances
We prove all randomized sampling methods produce outliers. Given a computable measure P over natural numbers or infinite binary sequences, there is no method that can produce an arbitrarily large sample such that all its members are typical of P. The second part of this dissertation describes a computationally inexpensive method to approximate Hilbertian distances. This method combines the semi-least squares inverse techinque with the canonical modern machine learning technique known as the kernel trick. In the task of distance approximation, our method was shown to be comparable in performance to a solution employing the Nystrom method. Using the kernel semi-least squares method, we developed and incorporated the Kernel-Subset-Tracker into the Camera Mouse, a video-based mouse replacement software for people with movement disabilities. The Kernel-Subset-Tracker is an exemplar-based method that uses a training set of representative images to produce online templates for positional tracking. Our experiments with test subjects show that augmenting the Camera Mouse with the Kernel-Subset-Tracker improves communication bandwidth statistically significantly
Towards high-accuracy augmented reality GIS for architecture and geo-engineering
L’architecture et la géo-ingénierie sont des domaines où les professionnels doivent prendre des décisions critiques. Ceux-ci requièrent des outils de haute précision pour les assister dans leurs tâches quotidiennes. La Réalité Augmentée (RA) présente un excellent potentiel pour ces professionnels en leur permettant de faciliter l’association des plans 2D/3D représentatifs des ouvrages sur lesquels ils doivent intervenir, avec leur perception de ces ouvrages dans la réalité. Les outils de visualisation s’appuyant sur la RA permettent d’effectuer ce recalage entre modélisation spatiale et réalité dans le champ de vue de l’usager. Cependant, ces systèmes de RA nécessitent des solutions de positionnement en temps réel de très haute précision. Ce n’est pas chose facile, spécialement dans les environnements urbains ou sur les sites de construction. Ce projet propose donc d’investiguer les principaux défis que présente un système de RA haute précision basé sur les panoramas omnidirectionels.Architecture and geo-engineering are application domains where professionals need to take critical decisions. These professionals require high-precision tools to assist them in their daily decision taking process. Augmented Reality (AR) shows great potential to allow easier association between the abstract 2D drawings and 3D models representing infrastructure under reviewing and the actual perception of these objects in the reality. The different visualization tools based on AR allow to overlay the virtual models and the reality in the field of view of the user. However, the architecture and geo-engineering context requires high-accuracy and real-time positioning from these AR systems. This is not a trivial task, especially in urban environments or on construction sites where the surroundings may be crowded and highly dynamic. This project investigates the accuracy requirements of mobile AR GIS as well as the main challenges to address when tackling high-accuracy AR based on omnidirectional panoramas
- …