30,703 research outputs found
Towards Visual Ego-motion Learning in Robots
Many model-based Visual Odometry (VO) algorithms have been proposed in the
past decade, often restricted to the type of camera optics, or the underlying
motion manifold observed. We envision robots to be able to learn and perform
these tasks, in a minimally supervised setting, as they gain more experience.
To this end, we propose a fully trainable solution to visual ego-motion
estimation for varied camera optics. We propose a visual ego-motion learning
architecture that maps observed optical flow vectors to an ego-motion density
estimate via a Mixture Density Network (MDN). By modeling the architecture as a
Conditional Variational Autoencoder (C-VAE), our model is able to provide
introspective reasoning and prediction for ego-motion induced scene-flow.
Additionally, our proposed model is especially amenable to bootstrapped
ego-motion learning in robots where the supervision in ego-motion estimation
for a particular camera sensor can be obtained from standard navigation-based
sensor fusion strategies (GPS/INS and wheel-odometry fusion). Through
experiments, we show the utility of our proposed approach in enabling the
concept of self-supervised learning for visual ego-motion estimation in
autonomous robots.Comment: Conference paper; Submitted to IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS) 2017, Vancouver CA; 8 pages, 8 figures,
2 table
Monte Carlo optimization of decentralized estimation networks over directed acyclic graphs under communication constraints
Motivated by the vision of sensor networks, we consider decentralized estimation networks over bandwidth–limited communication links, and are particularly interested in the tradeoff between the estimation accuracy and the cost of communications due to, e.g., energy consumption. We employ a class of in–network processing strategies that admits directed acyclic graph representations and yields a tractable Bayesian risk that comprises the cost of communications and estimation error penalty. This perspective captures a broad range of possibilities for processing under network constraints and enables a rigorous design problem in the form of constrained optimization. A similar scheme and the structures exhibited by the solutions have been previously studied in the context of decentralized detection. Under reasonable assumptions, the optimization can be carried out in a message passing fashion. We adopt
this framework for estimation, however, the corresponding optimization scheme involves integral operators that cannot be evaluated exactly in general. We develop an approximation framework using Monte Carlo methods and obtain
particle representations and approximate computational schemes for both the in–network processing strategies and their optimization. The proposed Monte Carlo optimization procedure operates in a scalable and efficient fashion and,
owing to the non-parametric nature, can produce results for any distributions provided that samples can be produced from the marginals. In addition, this approach exhibits graceful degradation of the estimation accuracy asymptotically
as the communication becomes more costly, through a parameterized Bayesian risk
Decentralized Estimation over Orthogonal Multiple-access Fading Channels in Wireless Sensor Networks - Optimal and Suboptimal Estimators
Optimal and suboptimal decentralized estimators in wireless sensor networks
(WSNs) over orthogonal multiple-access fading channels are studied in this
paper. Considering multiple-bit quantization before digital transmission, we
develop maximum likelihood estimators (MLEs) with both known and unknown
channel state information (CSI). When training symbols are available, we derive
a MLE that is a special case of the MLE with unknown CSI. It implicitly uses
the training symbols to estimate the channel coefficients and exploits the
estimated CSI in an optimal way. To reduce the computational complexity, we
propose suboptimal estimators. These estimators exploit both signal and data
level redundant information to improve the estimation performance. The proposed
MLEs reduce to traditional fusion based or diversity based estimators when
communications or observations are perfect. By introducing a general message
function, the proposed estimators can be applied when various analog or digital
transmission schemes are used. The simulations show that the estimators using
digital communications with multiple-bit quantization outperform the estimator
using analog-and-forwarding transmission in fading channels. When considering
the total bandwidth and energy constraints, the MLE using multiple-bit
quantization is superior to that using binary quantization at medium and high
observation signal-to-noise ratio levels
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