Object State Estimation Research Articles

Adaptive unscented Kalman filter for parameter and state estimation of nonlinear high-speed objects

An adaptive unscented Kalman filter (AUKF) and an augmented state method are employed to estimate the time-varying parameters and states of a kind of nonlinear high-speed objects. A strong tracking filter is employed to improve the tracking ability and robustness of unscented Kalman filter (UKF) when the process noise is inaccuracy, and wavelet transform is used to improve the estimate accuracy by the variance of measurement noise. An augmented square-root framework is utilized to improve the numerical stability and accuracy of UKF. Monte Carlo simulations and applications in the rapid trajectory estimation of hypersonic artillery shells confirm the effectiveness of the proposed method.

Research in Automated Planning and Control for Micromanipulation

Manipulation of microscopic objects, especially biological objects and microelectromechanical systems (MEMS) components, has become an important area of robotics research over the past several years. Automation is necessary as it is challenging to manually control the microobjects due to the scaling effect of the surface forces, stochastic motion of objects in fluid media, and uncertainty associated with object state estimation. Automation requires real-time control of the position, orientation, and force applied by each of the operational manipulators, as governed by the system-level objectives of optimizing resource, time, and effort, by planning suitable actions for the manipulated objects. In this paper, we provide a survey of the research in planning and control of such automated micromanipulation operations. We present a broad taxonomy based on the underlying approach, and discuss the salient features and experimental success of each research effort. We also identify the major limitations and common trends across all the approaches, discuss the effectiveness of an approach depending on the operation characteristics, and outline promising future research directions.

High Level Information Fusion (HLIF): Survey of models, issues, and grand challenges

High-level information fusion (situation and threat assessment, process and user refinement) requires novel solutions for the operational transition of information fusion designs. Low-level (signal processing, object state estimation and characterization) is well-vetted in the community as compared to high-level information fusion (control and relationships to the environment). Specific areas of interest include modeling (situations, environments), representations (semantic, knowledge, and complex), information management (ontologies, protocols) systems design (scenario-based, user-based, distributed-agent) and evaluation (measures of performance/effectiveness, and empirical case studies).

Probabilistic Joint State Estimation of Robot and Non-static Objects for Mobile Manipulation

Abstract In this paper, a unified and probabilistic method is proposed for simultaneously localization of a mobile service robot and states estimation of surrounding objects and co-existing people. This method allows intelligent robots to navigate reliably in dynamic environments and provide home-care services based on joint localization results. The algorithm makes use of probabilistic model to represent non-static people and objects states. Moreover, Rao-Blackwellized particle filters (RBPFs) are utilized for efficient joint estimation and laser sensing based smooth observation model is also introduced. The resulting algorithm works in real-time and estimates the position of people and state of doors with sufficient precision. Our approach has been tested in typical indoor environment with people, doors and other non-static objects. Experimental results demonstrate the favorable performance of the position estimation accuracy as well as the capability to deal with the uncertainty of mobile sensing.

Group Object Structure and State Estimation With Evolving Networks and Monte Carlo Methods

This paper proposes a technique for motion estimation of groups of targets based on evolving graph networks. The main novelty over alternative group tracking techniques stems from learning the network structure for the groups. Each node of the graph corresponds to a target within the group. The uncertainty of the group structure is estimated jointly with the group target states. New group structure evolving models are proposed for automatic graph structure initialization, incorporation of new nodes, unexisting nodes removal, and the edge update. Both the state and the graph structure are updated based on range and bearing measurements. This evolving graph model is propagated combined with a sequential Monte Carlo framework able to cope with measurement origin uncertainty. The effectiveness of the proposed approach is illustrated over scenarios for group motion estimation in urban environments. Results with challenging scenarios with merging, splitting, and crossing of groups are presented with high estimation accuracy. The performance of the algorithm is also evaluated and shown on real ground moving target indicator (GMTI) radar data and in the presence of data origin uncertainty.

Multiple object detection and tracking with pseudo-particle filter

To tackle the divergence of the classical particle filter method for multiple object tracking in image sequences, a new particle filter, called pseudoparticle filter (PPF), is proposed. The PPF invokes subset particles of generic particle filters to form a continuous estimate of the posterior density function of the objects. After sampling-importance resampling (SIR), the subset particles converge to the observations. It is proved that, using an appropriate kernel function of the mean shift algorithm, we can get the subset particles of the observations and the fixed points of clustering results as the state of the objects. A multiple object data association and state estimation technique is proposed to resolve the subset particles correspondence ambiguities that arise when multiple objects are present. Experimental results demonstrate the efficiency and effectiveness of the algorithm for single and multiple object tracking.

Information theoretic sensor data selection for active object recognition and state estimation

We introduce a formalism for optimal sensor parameter selection for iterative state estimation in static systems. Our optimality criterion is the reduction of uncertainty in the state estimation process, rather than an estimator-specific metric (e.g., minimum mean squared estimate error). The claim is that state estimation becomes more reliable if the uncertainty and ambiguity in the estimation process can be reduced. We use Shannon's information theory to select information-gathering actions that maximize mutual information, thus optimizing the information that the data conveys about the true state of the system. The technique explicitly takes into account the a priori probabilities governing the computation of the mutual information. Thus, a sequential decision process can be formed by treating the a priori probability at a certain time step in the decision process as the a posteriori probability of the previous time step. We demonstrate the benefits of our approach in an object recognition application using an active camera for sequential gaze control and viewpoint selection. We describe experiments with discrete and continuous density representations that suggest the effectiveness of the approach.

Probabilistic state estimation of dynamic objects with a moving mobile robot

Mobile service robots are designed to operate in dynamic and populated environments. To plan their missions and to perform them successfully, mobile robots need to keep track of relevant changes in the environment. For example, office delivery or cleaning robots must be able to estimate the state of doors or the position of waste-baskets in order to deal with the dynamics of the environment. In this paper we present a probabilistic technique for estimating the state of dynamic objects in the environment of a mobile robot. Our method matches real sensor measurements against expected measurements obtained by a sensor simulation to efficiently and accurately identify the most likely state of each object even if the robot is in motion. The probabilistic approach allows us to incorporate the robot’s uncertainty in its position into the state estimation process. The method has been implemented and tested on a real robot. We present different examples illustrating the efficiency and robustness of our approach.