70,811 research outputs found
Occlusion-Robust MVO: Multimotion Estimation Through Occlusion Via Motion Closure
Visual motion estimation is an integral and well-studied challenge in
autonomous navigation. Recent work has focused on addressing multimotion
estimation, which is especially challenging in highly dynamic environments.
Such environments not only comprise multiple, complex motions but also tend to
exhibit significant occlusion.
Previous work in object tracking focuses on maintaining the integrity of
object tracks but usually relies on specific appearance-based descriptors or
constrained motion models. These approaches are very effective in specific
applications but do not generalize to the full multimotion estimation problem.
This paper presents a pipeline for estimating multiple motions, including the
camera egomotion, in the presence of occlusions. This approach uses an
expressive motion prior to estimate the SE (3) trajectory of every motion in
the scene, even during temporary occlusions, and identify the reappearance of
motions through motion closure. The performance of this occlusion-robust
multimotion visual odometry (MVO) pipeline is evaluated on real-world data and
the Oxford Multimotion Dataset.Comment: To appear at the 2020 IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS). An earlier version of this work first
appeared at the Long-term Human Motion Planning Workshop (ICRA 2019). 8
pages, 5 figures. Video available at
https://www.youtube.com/watch?v=o_N71AA6FR
Sensor Management for Tracking in Sensor Networks
We study the problem of tracking an object moving through a network of
wireless sensors. In order to conserve energy, the sensors may be put into a
sleep mode with a timer that determines their sleep duration. It is assumed
that an asleep sensor cannot be communicated with or woken up, and hence the
sleep duration needs to be determined at the time the sensor goes to sleep
based on all the information available to the sensor. Having sleeping sensors
in the network could result in degraded tracking performance, therefore, there
is a tradeoff between energy usage and tracking performance. We design sleeping
policies that attempt to optimize this tradeoff and characterize their
performance. As an extension to our previous work in this area [1], we consider
generalized models for object movement, object sensing, and tracking cost. For
discrete state spaces and continuous Gaussian observations, we derive a lower
bound on the optimal energy-tracking tradeoff. It is shown that in the low
tracking error regime, the generated policies approach the derived lower bound
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
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