978 research outputs found
Counter-Hypothetical Particle Filters for Single Object Pose Tracking
Particle filtering is a common technique for six degree of freedom (6D) pose
estimation due to its ability to tractably represent belief over object pose.
However, the particle filter is prone to particle deprivation due to the
high-dimensional nature of 6D pose. When particle deprivation occurs, it can
cause mode collapse of the underlying belief distribution during importance
sampling. If the region surrounding the true state suffers from mode collapse,
recovering its belief is challenging since the area is no longer represented in
the probability mass formed by the particles. Previous methods mitigate this
problem by randomizing and resetting particles in the belief distribution, but
determining the frequency of reinvigoration has relied on hand-tuning abstract
heuristics. In this paper, we estimate the necessary reinvigoration rate at
each time step by introducing a Counter-Hypothetical likelihood function, which
is used alongside the standard likelihood. Inspired by the notions of
plausibility and implausibility from Evidential Reasoning, the addition of our
Counter-Hypothetical likelihood function assigns a level of doubt to each
particle. The competing cumulative values of confidence and doubt across the
particle set are used to estimate the level of failure within the filter, in
order to determine the portion of particles to be reinvigorated. We demonstrate
the effectiveness of our method on the rigid body object 6D pose tracking task.Comment: International Conference on Robotics and Automation (ICRA) 202
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
Navigating the Unknown: Uncertainty-Aware Compute-in-Memory Autonomy of Edge Robotics
This paper addresses the challenging problem of energy-efficient and
uncertainty-aware pose estimation in insect-scale drones, which is crucial for
tasks such as surveillance in constricted spaces and for enabling non-intrusive
spatial intelligence in smart homes. Since tiny drones operate in highly
dynamic environments, where factors like lighting and human movement impact
their predictive accuracy, it is crucial to deploy uncertainty-aware prediction
algorithms that can account for environmental variations and express not only
the prediction but also confidence in the prediction. We address both of these
challenges with Compute-in-Memory (CIM) which has become a pivotal technology
for deep learning acceleration at the edge. While traditional CIM techniques
are promising for energy-efficient deep learning, to bring in the robustness of
uncertainty-aware predictions at the edge, we introduce a suite of novel
techniques: First, we discuss CIM-based acceleration of Bayesian filtering
methods uniquely by leveraging the Gaussian-like switching current of CMOS
inverters along with co-design of kernel functions to operate with extreme
parallelism and with extreme energy efficiency. Secondly, we discuss the
CIM-based acceleration of variational inference of deep learning models through
probabilistic processing while unfolding iterative computations of the method
with a compute reuse strategy to significantly minimize the workload. Overall,
our co-design methodologies demonstrate the potential of CIM to improve the
processing efficiency of uncertainty-aware algorithms by orders of magnitude,
thereby enabling edge robotics to access the robustness of sophisticated
prediction frameworks within their extremely stringent area/power resources
Belief Scheduler based on model failure detection in the TBM framework. Application to human activity recognition.
International audienceA tool called Belief Scheduler is proposed for state sequence recognition in the Transferable Belief Model (TBM) framework. This tool makes noisy temporal belief functions smoother using a Temporal Evidential Filter (TEF). The Belief Scheduler makes belief on states smoother, separates the states (assumed to be true or false) and synchronizes them in order to infer the sequence. A criterion is also provided to assess the appropriateness between observed belief functions and a given sequence model. This criterion is based on the conflict information appearing explicitly in the TBM when combining observed belief functions with predictions. The Belief Scheduler is part of a generic architecture developed for on-line and automatic human action and activity recognition in videos of athletics taken with a moving camera. In experiments, the system is assessed on a database composed of 69 real athletics video sequences. The goal is to automatically recognize running, jumping, falling and standing-up actions as well as high jump, pole vault, triple jump and {long jump activities of an athlete. A comparison with Hidden Markov Models for video classification is also provided
Belief Functions: Theory and Algorithms
The subject of this thesis is belief function theory and its application in different contexts. Belief function theory can be interpreted as a generalization of Bayesian probability theory and makes it possible to distinguish between different types of uncertainty. In this thesis, applications of belief function theory are explored both on a theoretical and on an algorithmic level. The problem of exponential complexity associated with belief function inference is addressed in this thesis by showing how efficient algorithms can be developed based on Monte-Carlo approximations and exploitation of independence. The effectiveness of these algorithms is demonstrated in applications to particle filtering, simultaneous localization and mapping, and active classification
Multi-source Information Fusion Technology and Its Engineering Application
With the continuous development of information technology in recent years, information fusion technology, which originated from military applications, plays an important role in various fields. In addition, the rapidly increasing amount of data and the changing lifestyles of people in the information age are affecting the development of information fusion technology. More experts and scholars have focused their attention on the research of image or audio and video fusion or distributed fusion technology. This article summarizes the origin and development of information fusion technology and typical algorithms, as well as the future development trends and challenges of information fusion technology
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