Results 1  10
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171
The Unscented Particle Filter
, 2000
"... In this paper, we propose a new particle filter based on sequential importance sampling. The algorithm uses a bank of unscented filters to obtain the importance proposal distribution. This proposal has two very "nice" properties. Firstly, it makes efficient use of the latest available info ..."
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Cited by 211 (8 self)
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In this paper, we propose a new particle filter based on sequential importance sampling. The algorithm uses a bank of unscented filters to obtain the importance proposal distribution. This proposal has two very "nice" properties. Firstly, it makes efficient use of the latest available information and, secondly, it can have heavy tails. As a result, we find that the algorithm outperforms standard particle filtering and other nonlinear filtering methods very substantially. This experimental finding is in agreement with the theoretical convergence proof for the algorithm. The algorithm also includes resampling and (possibly) Markov chain Monte Carlo (MCMC) steps.
Robocentric map joining: Improving the consistency of EKFSLAM
, 2007
"... In this paper we study the Extended Kalman Filter approach to simultaneous localization and mapping (EKFSLAM), describing its known properties and limitations, and concentrate on the filter consistency issue. We show that linearization of the inherent nonlinearities of both the vehicle motion and ..."
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Cited by 39 (3 self)
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In this paper we study the Extended Kalman Filter approach to simultaneous localization and mapping (EKFSLAM), describing its known properties and limitations, and concentrate on the filter consistency issue. We show that linearization of the inherent nonlinearities of both the vehicle motion and the sensor models frequently drives the solution of the EKFSLAM out of consistency, specially in those situations where uncertainty surpasses a certain threshold. We propose a mapping algorithm, Robocentric Map Joining, which improves consistency of the EKFSLAM algorithm by limiting the level of uncertainty in the continuous evolution of the stochastic map: (1) by building a sequence of independent local maps, and (2) by using a robot centered representation of each local map. Simulations and a largescale indoor/outdoor experiment validate the proposed approach.
A PhysicallyBased Motion Retargeting Filter
, 2003
"... This paper presents a novel constraintbased motion editing technique. On the basis of animatorspecified kinematic and dynamic constraints, the method converts a given captured or animated motion to a physically plausible motion. In contrast to previous methods using spacetime optimization, we cast ..."
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Cited by 36 (0 self)
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This paper presents a novel constraintbased motion editing technique. On the basis of animatorspecified kinematic and dynamic constraints, the method converts a given captured or animated motion to a physically plausible motion. In contrast to previous methods using spacetime optimization, we cast the motion editing problem as a constrained state estimation problem based on the perframe Kalman filter framework. The method works as a filter that sequentially scans the input motion to produce a stream of output motion frames at a stable interactive rate. Animators can tune several filter parameters to adjust to different motions, or can turn the constraints on or off based on their contributions to the final re sult. One particularly appealing feature of the proposed technique is that animators find it very scalable and intuitive. Experiments on various systems show that the technique processes the motions of a human with 54 degrees of freedom at about 150 fps when only kinematic constraints are applied, and at about 10 fps when both kinematic and dynamic constraints are applied. Experiments on various types of motion show that the proposed method produces remarkably realistic animations.
SigmaPoint Kalman Filters For Integrated Navigation
, 2004
"... Core to integrated navigation systems is the concept of fusing noisy observations from GPS, Inertial Measurement Units (IMU), and other available sensors. The current industry standard and most widely used algorithm for this purpose is the extended Kalman filter (EKF) [6]. The EKF combines the senso ..."
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Cited by 33 (1 self)
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Core to integrated navigation systems is the concept of fusing noisy observations from GPS, Inertial Measurement Units (IMU), and other available sensors. The current industry standard and most widely used algorithm for this purpose is the extended Kalman filter (EKF) [6]. The EKF combines the sensor measurements with predictions coming from a model of vehicle motion (either dynamic or kinematic), in order to generate an estimate of the current navigational state (position, velocity, and attitude). This paper points out the inherent shortcomings in using the EKF and presents, as an alternative, a family of improved derivativeless nonlinear Kalman filters called sigmapoint Kalman filters (SPKF). We demonstrate the improved state estimation performance of the SPKF by applying it to the problem of loosely coupled GPS/INS integration. A novel method to account for latency in the GPS updates is also developed for the SPKF (such latency compensation is typically inaccurate or not practical with the EKF). A UAV (rotorcraft) test platform is used to demonstrate the results. Performance metrics indicate an approximate 30% error reduction in both attitude and position estimates relative to the baseline EKF implementation.
Automating the Implementation of Kalman Filter Algorithms
 ACM Transactions on Mathematical Software
"... AUTOFILTER is a tool that generates implementations that solve state estimation problems using Kalman filters. From a highlevel, mathematicsbased description of a state estimation problem, AUTOFILTER automatically generates code that computes a statistically optimal estimate using one or more of a ..."
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Cited by 31 (7 self)
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AUTOFILTER is a tool that generates implementations that solve state estimation problems using Kalman filters. From a highlevel, mathematicsbased description of a state estimation problem, AUTOFILTER automatically generates code that computes a statistically optimal estimate using one or more of a number of wellknown variants of the Kalman filter algorithm. The problem description may be given in terms of continuous or discrete, linear or nonlinear process and measurement dynamics. From this description, AUTOFILTER automates many common solution methods (e.g., linearization, discretization) and generates C or Matlab code fully automatically. AUTOFILTER surpasses toolkitbased programming approaches for Kalman filters because it requires no lowlevel programming skills (e.g., to “glue ” together library function calls). AUTOFILTER raises the level of discourse to the mathematics of the problem at hand rather than the details of what algorithms, data structures, optimizations and so on are required to implement it. An overview of AUTOFILTER is given along with an example of its practical application to deep space attitude estimation.
Bayesian state and parameter estimation of uncertain dynamical systems. Probabilistic Engineering Mechanics
, 2006
"... The focus of this paper is Bayesian state and parameter estimation using nonlinear models. A recently developed method, the particle filter, is studied that is based on stochastic simulation. Unlike the wellknown extended Kalman filter, the particle filter is applicable to highly nonlinear systems ..."
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Cited by 25 (0 self)
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The focus of this paper is Bayesian state and parameter estimation using nonlinear models. A recently developed method, the particle filter, is studied that is based on stochastic simulation. Unlike the wellknown extended Kalman filter, the particle filter is applicable to highly nonlinear systems with nonGaussian uncertainties. Recently developed techniques that improve the convergence of the particle filter simulations are introduced and discussed. Comparisons between the particle filter and the extended Kalman filter are made using several numerical examples of nonlinear systems. The results indicate that the particle filter provides consistent state and parameter estimates for highly nonlinear systems, while the extended Kalman filter does not.
Partial Linear Gaussian Models for Tracking in Image Sequences Using Sequential Monte Carlo Methods
 JOURNAL OF COMPUTER VISION
, 2007
"... The recent development of Sequential Monte Carlo methods (also called particle filters) has enabled the definition of efficient algorithms for tracking applications in image sequences. The efficiency of these approaches depends on the quality of the statespace exploration, which may be inefficient ..."
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Cited by 22 (3 self)
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The recent development of Sequential Monte Carlo methods (also called particle filters) has enabled the definition of efficient algorithms for tracking applications in image sequences. The efficiency of these approaches depends on the quality of the statespace exploration, which may be inefficient due to a crude choice of the function used to sample in the associated probability space. A careful study of this issue led us to consider the modeling of the tracked dynamic system with partial linear Gaussian models. Such models are characterized by a non linear dynamic equation, a linear measurement equation and additive Gaussian noises. They allow inferring an analytic expression of the optimal importance function used in the diffusion process of the particle filter, and enable building a relevant approximation of a validation gate. Despite of these potential advantages partial linear Gaussian models have not been investigated. The aim of this paper is therefore to demonstrate that such models can be of real interest facing difficult usual issues such as occlusions, ambiguities due to cluttered backgrounds and large state space. Three instances of these models are proposed. After a theoretical analysis, their significance is demonstrated by their performance for tracking points and planar objects in challenging realworld image sequences.
Sigmapoint Kalman filtering for integrated GPS and inertial navigation,”
 in AIAA Guidance, Navigation and Contol Conference,
, 2005
"... A sigmapoint Kalman filter is derived for integrating GPS measurements with inertial measurements from gyros and accelerometers to determine both the position and the attitude of a moving vehicle. Sigmapoint filters use a carefully selected set of sample points to more accurately map the probabil ..."
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Cited by 21 (1 self)
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A sigmapoint Kalman filter is derived for integrating GPS measurements with inertial measurements from gyros and accelerometers to determine both the position and the attitude of a moving vehicle. Sigmapoint filters use a carefully selected set of sample points to more accurately map the probability distribution than the linearization of the standard extended Kalman filter, leading to faster convergence from inaccurate initial conditions in position/attitude estimation problems. The filter formulation is based on standard inertial navigation equations. The global attitude parameterization is given by a quaternion, while a generalized threedimensional attitude representation is used to define the local attitude error. A multiplicative quaternionerror approach is used to guarantee that quaternion normalization is maintained in the filter. Simulation and experimental results are shown to compare the performance of the sigmapoint filter with a standard extended Kalman filter approach.
A flexible and scalable slam system with full 3d motion estimation
 in International Symposium on Safety, Security, and Rescue Robotics. IEEE
, 2011
"... Abstract—For many applications in Urban Search and Rescue (USAR) scenarios robots need to learn a map of unknown environments. We present a system for fast online learning of occupancy grid maps requiring low computational resources. It combines a robust scan matching approach using a LIDAR system w ..."
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Cited by 20 (3 self)
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Abstract—For many applications in Urban Search and Rescue (USAR) scenarios robots need to learn a map of unknown environments. We present a system for fast online learning of occupancy grid maps requiring low computational resources. It combines a robust scan matching approach using a LIDAR system with a 3D attitude estimation system based on inertial sensing. By using a fast approximation of map gradients and a multiresolution grid, reliable localization and mapping capabilities in a variety of challenging environments are realized. Multiple datasets showing the applicability in an embedded handheld mapping system are provided. We show that the system is sufficiently accurate as to not require explicit loop closing techniques in the considered scenarios. The software is available as an open source package for ROS.
Pose estimation and adaptive robot behaviour for humanrobot interaction
 in Proc. ICRA
"... Abstract — This paper introduces a new method to determine a person’s pose based on laser range measurements. Such estimates are typically a prerequisite for any humanaware robot navigation, which is the basis for effective and timeextended interaction between a mobile robot and a human. The robot ..."
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Cited by 12 (4 self)
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Abstract — This paper introduces a new method to determine a person’s pose based on laser range measurements. Such estimates are typically a prerequisite for any humanaware robot navigation, which is the basis for effective and timeextended interaction between a mobile robot and a human. The robot uses observed information from a laser range finder to detect persons and their position relative to the robot. This information together with the motion of the robot itself is fed through a Kalman filter, which utilizes a model of the human kinematic movement to produce an estimate of the person’s pose. The resulting pose estimates are used to identify humans who wish to be approached and interacted with. The behaviour of the robot is based on adaptive potential functions adjusted accordingly such that the persons social spaces are respected. The method is tested in experiments that demonstrate the potential of the combined pose estimation and adaptive behaviour approach. I.