319 research outputs found
Vision-Aided Inertial Navigation
This document discloses, among other things, a system and method for implementing an algorithm to determine pose, velocity, acceleration or other navigation information using feature tracking data. The algorithm has computational complexity that is linear with the number of features tracked
Analytically-selected multi-hypothesis incremental MAP estimation
In this paper, we introduce an efficient maximum a posteriori (MAP) estimation algorithm, which effectively tracks multiple most probable hypotheses. In particular, due to multimodal distributions arising in most nonlinear problems, we employ a bank of MAP to track these modes (hypotheses). The key idea is that we analytically determine all the posterior modes for the current state at each time step, which are used to generate highly probable hypotheses for the entire trajectory. Moreover, since it is expensive to solve the MAP problem sequentially over time by an iterative method such as Gauss-Newton, in order to speed up its solution, we reuse the previous computations and incrementally update the square-root informationmatrix at every time step, while batch relinearization is performed only periodically or as needed.United States. Office of Naval Research (Grant N00014-10-1-0936)United States. Office of Naval Research (Grant N00014-11-1-0688)United States. Office of Naval Research (Grant N00014-12-10020)National Science Foundation (U.S.) (IIS-0643680
Surface Normal Estimation of Tilted Images via Spatial Rectifier
In this paper, we present a spatial rectifier to estimate surface normals of
tilted images. Tilted images are of particular interest as more visual data are
captured by arbitrarily oriented sensors such as body-/robot-mounted cameras.
Existing approaches exhibit bounded performance on predicting surface normals
because they were trained using gravity-aligned images. Our two main hypotheses
are: (1) visual scene layout is indicative of the gravity direction; and (2)
not all surfaces are equally represented by a learned estimator due to the
structured distribution of the training data, thus, there exists a
transformation for each tilted image that is more responsive to the learned
estimator than others. We design a spatial rectifier that is learned to
transform the surface normal distribution of a tilted image to the rectified
one that matches the gravity-aligned training data distribution. Along with the
spatial rectifier, we propose a novel truncated angular loss that offers a
stronger gradient at smaller angular errors and robustness to outliers. The
resulting estimator outperforms the state-of-the-art methods including data
augmentation baselines not only on ScanNet and NYUv2 but also on a new dataset
called Tilt-RGBD that includes considerable roll and pitch camera motion.Comment: 16 page
Performance analysis of twin-spool water injected gas turbines using adaptive modeling.
ABSTRACT The development of an adaptive performance model for multi-spool gas turbines, equipped with the possibility of water injection is described. The model covers water injection at engine inlet, between the compressors and at the compressor exit. The selection of modification factors and the procedure for adapting component characteristics to overall performance data is discussed. A case of adaptation to overall performances is presented. The use of the model for studying overall engine and components performance is demonstrated. It is shown how operation with water injection modifies component operation, a fact that allows the identification of a wider range of the performance characteristics, in comparison to dry operation. This fact may also increase the diagnostic ability of techniques employing adaptive models. The sensitivity of diagnostic procedures to the different modes of operation of a gas turbine of the type described in the paper is also discussed
Stochastic cloning: a generalized framework for processing relative state measurements
Introduces a generalized framework, termed "stochastic cloning," for processing relative state measurements within a Kalman filter estimator. The main motivation and application for this methodology is the problem of fusing displacement measurements with position estimates for mobile robot localization. Previous approaches have ignored the developed interdependencies (cross-correlation terms) between state estimates of the same quantities at different time instants. By directly expressing relative state measurements in terms of previous and current state estimates, the effect of these crosscorrelation terms on the estimation process is analyzed and considered during updates. Simulation and experimental results validate this approach
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