A Robust Solution to the Stereo-Vision-Based Simultaneous Localization and Mapping Problem with Steady and Moving Landmarks

The problem of visual simultaneous localization and mapping (SLAM) is examined in this paper using recently developed ideas and algorithms from modern robust control and estimation theory. A nonlinear model for a stereo-vision-based sensor is derived that leads to nonlinear measurements of the landmark coordinates along with optical flow-based measurements of the relative robot–landmark velocity. Using a novel analytical measurement transformation, the nonlinear SLAM problem is converted into the linear domain and solved using a robust linear filter. Actually, the linear filter is guaranteed stable and the SLAM state estimation error is bounded within an ellipsoidal set. A mathematically rigorous stability proof is given that holds true even when the landmarks move in accordance with an unknown control input. No similar results are available for the commonly employed extended Kalman filter, which is known to exhibit divergence and inconsistency characteristics in practice. A number of illustrative examples are given using both simulated and real vision data that further validate the proposed method.     

Belief-Driven Manipulator Visual Servoing for Less Controlled Environments

This paper presents the architecture of a feedforward manipulator control strategy based on a belief function that may be appropriate for less controlled environments. In this architecture, the belief about the environmental state, as described by a probability density function, is maintained by a recursive Bayesian estimation process. A likelihood is derived from each observation regardless of whether the targeted features of the environmental state have been detected or not. This provides continuously evolving information to the controller and allows an inaccurate belief to evolve into an accurate belief. Control actions are determined by maximizing objective functions using non-linear optimization. Forward models are used to transform control actions to a predicted state so that objective functions may be expressed in task space. The first set of examples numerically investigates the validity of the proposed strategy by demonstrating control in a two dimensional scenario. Then a more realistic application is presented where a robotic manipulator executes a searching and tracking task using an eye-in-hand vision sensor.     

Exact non-linear Bayesian parameter estimation for autonomous compliant motion

This paper presents theoretical and experimental results for the estimation of large position and orientation inaccuracies during force-controlled compliant motion. This is a significant improvement over previous results. The estimation is based on position, velocity and force measurements. For large position and orientation inaccuracies, the non-linear estimation problem is not satisfactorily solved by existing Kalman filters. Therefore, a new Bayesian estimator is derived. The filter is derived independently of our application and is valid for static systems (parameter estimation) with any kind of non-linear measurement equation subject to Gaussian measurement uncertainty and for a limited class of dynamic systems. Experimental results for the estimation of the inaccurately known positions and orientations of contacting objects during autonomous compliant motion are presented.     

9—THE CONSISTENCY OF THE VALUATIONS OF GREASY WOOL BY SIX APPRAISERS IN A WOOL-BUYING FIRM

A study is made of the consistency of six woolbuyers' estimations of type, yield, and valuation of greasy wool. Parameters for consistency and accuracy are estimated and intra-class correlation coefficients used to rank the buyers within the firm. The results indicate that changes in greasy-price limits are influenced more by changes in type appraisal than by changes in yield estimation. The method is discussed as a means of regularly checking the standard and improvement of buyers in a firm.     

IMPROVED METHOD FOR HILBERT INSTANTANEOUS FREQUENCY ESTIMATION

In the mechanical fault detection and diagnosis field, it is more and more important to analyze the instantaneous frequency (IF) character of complex vibration signal. The improved IF estimation method is put forward aiming at the shortage of traditional Hilbert transform. It is based on Hilbert transform in wavelet domain. With the help of relationship between the real part and the imaginary part obtained from the complex coefficient of continuous wavelet transform or the analytical signal reconstructed in wavelet packet decomposition, the instantaneous phase function of the subcomponent is extracted. In order to improve the precise of IF estimated out, some means such as Linear regression, adaptive filtering, resampling are applied into the instantaneous phase obtained, then, the central differencing operator is used to get desired IF. Simulation results with synthetic and gearbox fault signals are included to illustrate the proposed method.     

Parallel Implementations of Block-Based Motion Vector Estimation for Video Compression on Four Parallel Processing Systems

Parallel algorithms, based on a distributed memory machine model, for an exhaustive search technique for motion vector estimation in video compression are being designed and evaluated. Results from the execution on a 16,384 processor MasPar MP-1 (an SIMD machine), a 140 node Intel Paragon XP/S and a 16 node IBM SP2 (two M IMD machines), and the 16 processor PASM prototype (a partitionable SIMD/MIMD mixed-mode machine) are presented. The trade-offs of using different modes of parallelism (SIMD, SPMD, and mixed-mode) and different data partitioning schemes (the rectangular and stripe subimage methods) are examined. The analytical and experimental results shown in this application study will help practitioners to predict and contrast the performance of different approaches to parallel implementation of this important video compression technique. The results presented are also applicable to a large class of image and video processing tasks. Case studies, such as the one presented here, are a necessary step in developing software tools for mapping an application task onto a single parallel machine and for mapping a set of independent application tasks, or the subtasks of a single application task, onto a heterogeneous suite of parallel machines.     

Effect of alumina pickup on mould flux viscosity in continuous slab casting

Abstract

Mould flux samples were taken from a continuous slab caster mould at various stages during casting sequences. These confirmed that the amount of alumina pickup reaches a steady state level within the first 40 min of casting (in agreement with a simple model of alumina pickup). The steady state level was 3–4 mass-% higher than the original alumina content of the flux. Viscosity measurements carried out on two commercial mould fluxes, with different levels of alumina added, showed that the viscosity increase as a result of a 4 mass-%increase in alumina is not likely to affect mould behaviour. Comparison of the measured viscosities with predictions from literature models showed that no single literature model corresponds consistently to the measured values.     

10—THE VALIDITY OF PRESSURE-CORING FOR ESTIMATION OF THE CONTENT OF NOOGOORA BURR IN WOOL BALES

Experiments are described to establish the validity of samples, for Noogoora-burr determination, taken from wool bales with pressure-coring tools. Coring tools of ¾-in. and larger diameters give unbiassed samples for the determination of both Noogoora burr and burr medic. The ½-in.-diameter tool, although previously shown to give unbiassed samples for the determination of burr medic, is subject to some bias in the sampling of bales containing Noogoora burr.     

A Time-of-Arrival-Based Positioning Technique With Non-Line-of-Sight Mitigation Using Low-Frequency Sound

This paper presents a non-line-of-sight (NLOS) localization technique and its system for a quiet and known environment that uses first-arrival low-frequency acoustic signals. The proposed technique first measures the time-of-arrivals (ToAs) of the first-arrival low-frequency sounds. The technique then estimates the location of a target by minimizing the differences between the measured ToAs and the corresponding ToAs estimated from the known map. The strength of the proposed technique is that the positioning accuracy is not corrupted by any small or known obstacle. The performance of the technique was investigated by its comparison to other techniques in both simulations and an experiment. The numerical results show successful localization of a target in various situations, demonstrating the efficacy of the technique and system as a feasible solution for NLOS mitigation.