18,448 research outputs found
Motion Conflict Detection and Resolution in Visual-Inertial Localization Algorithm
In this dissertation, we have focused on conflicts that occur due to disagreeing motions in multi-modal localization algorithms. In spite of the recent achievements in robust localization by means of multi-sensor fusion, these algorithms are not applicable to all environments. This is primarily attributed to the following fundamental assumptions: (i) the environment is predominantly stationary, (ii) only ego-motion of the sensor platform exists, and (iii) multiple sensors are always in agreement with each other regarding the observed motion. Recently, studies have shown how to relax the static environment assumption using outlier rejection techniques and dynamic object segmentation. Additionally, to handle non ego-motion, approaches that extend the localization algorithm to multi-body tracking have been studied. However, there has been no attention given to the conditions where multiple sensors contradict each other with regard to the motions observed.
Vision based localization has become an attractive approach for both indoor and outdoor applications due to the large information bandwidth provided by images and reduced cost of the cameras used. In order to improve the robustness and overcome the limitations of vision, an Inertial Measurement Unit (IMU) may be used. Even though visual-inertial localization has better accuracy and improved robustness due to the complementary nature of camera and IMU sensor, they are affected by disagreements in motion observations. We term such dynamic situations as environments with motion conflictbecause these are caused when multiple different but self- consistent motions are observed by different sensors. Tightly coupled visual inertial fusion approaches that disregard such challenging situations exhibit drift that can lead to catastrophic errors.
We have provided a probabilistic model for motion conflict. Additionally, a novel algorithm to detect and resolve motion conflicts is also presented. Our method to detect motion conflicts is based on per-frame positional estimate discrepancy and per- landmark reprojection errors. Motion conflicts were resolved by eliminating inconsistent IMU and landmark measurements. Finally, a Motion Conflict aware Visual Inertial Odometry (MC- VIO) algorithm that combined both detection and resolution of motion conflict was implemented. Both quantitative and qualitative evaluation of MC-VIO on visually and inertially challenging datasets were obtained. Experimental results indicated that MC-VIO algorithm reduced the absolute trajectory error by 70% and the relative pose error by 34% in scenes with motion conflict, in comparison to the reference VIO algorithm. Motion conflict detection and resolution enables the application of visual inertial localization algorithms to real dynamic environments. This paves the way for articulate object tracking in robotics. It may also find numerous applications in active long term augmented reality
A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks
Multisensor fusion and consensus filtering are two fascinating subjects in the research of sensor networks. In this survey, we will cover both classic results and recent advances developed in these two topics. First, we recall some important results in the development ofmultisensor fusion technology. Particularly, we pay great attention to the fusion with unknown correlations, which ubiquitously exist in most of distributed filtering problems. Next, we give a systematic review on several widely used consensus filtering approaches. Furthermore, some latest progress on multisensor fusion and consensus filtering is also presented. Finally,
conclusions are drawn and several potential future research directions are outlined.the Royal Society of the UK, the National Natural Science Foundation of China under Grants 61329301, 61374039, 61304010, 11301118, and 61573246, the Hujiang Foundation of China under Grants C14002
and D15009, the Alexander von Humboldt Foundation of Germany, and the Innovation Fund Project for Graduate Student of Shanghai under Grant JWCXSL140
Online Tool Condition Monitoring Based on Parsimonious Ensemble+
Accurate diagnosis of tool wear in metal turning process remains an open
challenge for both scientists and industrial practitioners because of
inhomogeneities in workpiece material, nonstationary machining settings to suit
production requirements, and nonlinear relations between measured variables and
tool wear. Common methodologies for tool condition monitoring still rely on
batch approaches which cannot cope with a fast sampling rate of metal cutting
process. Furthermore they require a retraining process to be completed from
scratch when dealing with a new set of machining parameters. This paper
presents an online tool condition monitoring approach based on Parsimonious
Ensemble+, pENsemble+. The unique feature of pENsemble+ lies in its highly
flexible principle where both ensemble structure and base-classifier structure
can automatically grow and shrink on the fly based on the characteristics of
data streams. Moreover, the online feature selection scenario is integrated to
actively sample relevant input attributes. The paper presents advancement of a
newly developed ensemble learning algorithm, pENsemble+, where online active
learning scenario is incorporated to reduce operator labelling effort. The
ensemble merging scenario is proposed which allows reduction of ensemble
complexity while retaining its diversity. Experimental studies utilising
real-world manufacturing data streams and comparisons with well known
algorithms were carried out. Furthermore, the efficacy of pENsemble was
examined using benchmark concept drift data streams. It has been found that
pENsemble+ incurs low structural complexity and results in a significant
reduction of operator labelling effort.Comment: this paper has been published by IEEE Transactions on Cybernetic
Application of probabilistic PCR5 Fusion Rule for Multisensor Target Tracking
This paper defines and implements a non-Bayesian fusion rule for combining
densities of probabilities estimated by local (non-linear) filters for tracking
a moving target by passive sensors. This rule is the restriction to a strict
probabilistic paradigm of the recent and efficient Proportional Conflict
Redistribution rule no 5 (PCR5) developed in the DSmT framework for fusing
basic belief assignments. A sampling method for probabilistic PCR5 (p-PCR5) is
defined. It is shown that p-PCR5 is more robust to an erroneous modeling and
allows to keep the modes of local densities and preserve as much as possible
the whole information inherent to each densities to combine. In particular,
p-PCR5 is able of maintaining multiple hypotheses/modes after fusion, when the
hypotheses are too distant in regards to their deviations. This new p-PCR5 rule
has been tested on a simple example of distributed non-linear filtering
application to show the interest of such approach for future developments. The
non-linear distributed filter is implemented through a basic particles
filtering technique. The results obtained in our simulations show the ability
of this p-PCR5-based filter to track the target even when the models are not
well consistent in regards to the initialization and real cinematic
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