Ashwin Dani | University of Connecticut-storrs (original) (raw)

Papers by Ashwin Dani

Research paper thumbnail of Method and Apparatus for Nonlinear Dynamic Estimation of Feature Depth Using Calibrated Moving Cameras

Research paper thumbnail of Lyapunov-Based Nonlinear Estimation Methods With Applications to Machine Vision

ABSTRACT Recent advances in image-based information estimation has enabled the use of vision sens... more ABSTRACT Recent advances in image-based information estimation has enabled the use of vision sensor in many robotics and surveillance applications. The work in this dissertation, is focused on developing online techniques for image-based structure and motion (SaM) estimation. Since traditional batch methods are not useful for the online vision-based control tasks, observer-based approaches to the problem have been developed. Starting from the Kalman-filter for SaM problem by L. Matthies, many contributions to the observer approach for the SaM problem exist in literature. Various models are introduced in literature for SaM estimation but two models are prevalent, viz; a kinematic relative motion affine model with implicit outputs and a transformed nonlinear state model with the linear output equation. The existing SaM observers are designed for the case of a stationary object, requires full camera velocity information and cannot be used for certain camera motions. In this dissertation, new solutions to the SaM are presented using the transformed nonlinear state model which can be used for larger set of camera motions, does not require full camera velocity information, and are reduced-order. Solutions for the stationary as well as moving objects viewed by a moving camera are presented. In Chapter 3, a reduced order observer is developed to estimate the structure of a static object using a moving camera, where full camera velocity and linear acceleration are known. Chapter 4 focuses on the development of a reduced order observer for the SaM estimation of a stationary object when only a single camera linear velocity is known. In Chapter 5, an observer design is presented for a specific class of nonlinear systems where the output dynamics are affine in the unmeasurable state and the dynamics of the unmeasurable state are nonlinear. The method is applied to simultaneously estimate the structure and motion of a moving object seen by a moving camera. Another strategy to the observer design in the presence of an unmeasurable disturbance is an unknown input observer (UIO). Chapter 6 provides a solution to an UIO design for a general class of nonlinear systems and it's application to structure estimation of a moving object is shown in Chapter 7.

Research paper thumbnail of Gyro-aided image-based tracking using mutual information optimization and user inputs

2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2014

Research paper thumbnail of Dept. of Aerosp. Eng., Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA

Research paper thumbnail of Single Camera Structure and Motion Estimation

Lecture Notes in Control and Information Sciences, 2010

Two new continuous nonlinear observers are proposed for the problem of structure from motion (SfM... more Two new continuous nonlinear observers are proposed for the problem of structure from motion (SfM) and structure and motion (SaM) of a stationary object observed by a moving camera. The observer for SfM, where full velocity feedback is available, yields global exponential convergence of the states for the structure. The SaM observer requires only one of the linear velocities as a feedback and identifies the states asymptotically. The linear velocity is used to derive the scene scale information. The observer gain conditions are ...

Research paper thumbnail of Camera motion estimation for 3-D structure reconstruction of moving objects

2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2012

Abstract The method for camera motion estimation is proposed for the moving objects. Whereas the ... more Abstract The method for camera motion estimation is proposed for the moving objects. Whereas the estimation of the structure and motion (SaM) of the moving objects usually involves the constraints on the motion of the camera and the object, the moving camera velocities can be estimated in our work using the images of the moving object from the single camera without any constraint on the camera and object motion. To this end, the dynamics of the partially measurable state are arranged in such a way that the recursive ...

Research paper thumbnail of Inertial-Aided Vision-Based Localization and Mapping in a Riverine Environment with Reflection Measurements

AIAA Guidance, Navigation, and Control (GNC) Conference, 2013

Research paper thumbnail of Image moments for higher-level feature based navigation

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013

ABSTRACT This paper presents a novel vision-based localization and mapping algorithm using image ... more ABSTRACT This paper presents a novel vision-based localization and mapping algorithm using image moments of region features. The environment is represented using regions, such as planes and/or 3D objects instead of only a dense set of feature points. The regions can be uniquely defined using a small number of parameters; e.g., a plane can be completely characterized by normal vector and distance to a local coordinate frame attached to the plane. The variation of image moments of the regions in successive images can be related to the parameters of the regions. Instead of tracking a large number of feature points, variations of image moments of regions can be computed by tracking the segmented regions or a few feature points on the objects in successive images. A map represented by regions can be characterized using a minimal set of parameters. The problem is formulated as a nonlinear filtering problem. A new discrete-time nonlinear filter based on the state-dependent coefficient (SDC) form of nonlinear functions is presented. It is shown via Monte-Carlo simulations that the new nonlinear filter is more accurate and consistent than EKF by evaluating the root-mean squared error (RMSE) and normalized estimation error squared (NEES).

Research paper thumbnail of Observer design for stochastic nonlinear systems using contraction analysis

2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012

ABSTRACT This paper presents a new observer for Itô stochastic nonlinear systems with guaranteed ... more ABSTRACT This paper presents a new observer for Itô stochastic nonlinear systems with guaranteed stability. Contraction analysis is used to analyze incremental stability of the observer for an Itô stochastic nonlinear system. A bound on the mean squared distance between the trajectories of original dynamics and the observer dynamics is obtained as a function of contraction rate and maximum noise intensity. The observer design is based on non-unique state-dependent coefficient (SDC) forms which parametrize the nonlinearity in an extended linear form. In this paper, a convex combination of several parametrizations is used. An optimization problem with state-dependent linear matrix inequality (SDLMI) constraints is formulated to select the free parameters of the convex combination for achieving faster convergence and robustness against disturbances. Moreover, the L2 norm of the disturbance and noise to the estimation error is shown to be finite. The present algorithm shows improved performance in comparison to the extended Kalman filter (EKF) and the state-dependent differential Riccati equation (SDDRE) filter in simulation.

Research paper thumbnail of Globally exponentially convergent observer for vision-based range estimation

2010 IEEE International Symposium on Intelligent Control, 2010

Research paper thumbnail of Nonlinear estimation of gait kinematics during functional electrical stimulation and orthosis-based walking

2014 American Control Conference, 2014

ABSTRACT This paper presents a nonlinear estimation algorithm which utilizes a low-degree of free... more ABSTRACT This paper presents a nonlinear estimation algorithm which utilizes a low-degree of freedom model of functional electrical stimulation (FES) and orthosis-based walking to estimate lower-limb angles. The estimated lower limb angles can be used to decide when the FES signal should be applied to the leg during the different phases of walking. To this end, we use measurements from inertial measurement units (IMUs) to estimate the lower limb segment angles. A state-dependent coefficient (SDC)-based nonlinear estimator is developed to estimate the lower limb angles. The nonlinear estimator is robust to uncertainties in the motion modeling and sensor noise/bias from the IMUs. A comparison with extended Kalman (EKF)-like filter shows improved performance of the estimator in simulation studies.

Research paper thumbnail of Information fusion in human-robot collaboration using neural network representation

2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2014

Research paper thumbnail of Globally exponentially stable observer for vision-based range estimation

Research paper thumbnail of Single Camera Structure and Motion

IEEE Transactions on Automatic Control, 2012

Abstract A reduced order nonlinear observer is proposed for the problem of “structure and motion ... more Abstract A reduced order nonlinear observer is proposed for the problem of “structure and motion (SaM)” estimation of a stationary object observed by a moving calibrated camera. In comparison to existing work which requires some knowledge of the Euclidean geometry of an observed object or full knowledge of the camera motion, the developed reduced order observer only requires one camera linear velocity and corresponding acceleration to asymptotically identify the Euclidean coordinates of the feature points attached to an ...

Research paper thumbnail of Network Connectivity Preserving Formation Stabilization and Obstacle Avoidance via a Decentralized Controller

IEEE Transactions on Automatic Control, 2000

Research paper thumbnail of Saturated control of an uncertain nonlinear system with input delay

Automatica, 2013

ABSTRACT This paper examines saturated control of a general class of uncertain nonlinear systems ... more ABSTRACT This paper examines saturated control of a general class of uncertain nonlinear systems with time-delayed actuation and additive bounded disturbances. The bound on the control is known a priori and can be adjusted by changing the feedback gains. A Lyapunov-based stability analysis utilizing Lyapunov–Krasovskii (LK) functionals is provided to prove uniformly ultimately bounded tracking despite uncertainties in the dynamics. A numerical example is presented to demonstrate the performance of the controller.

Research paper thumbnail of Systems and Methods for Estimating the Structure and Motion of an Object

Abstract: In one embodiment, the structure and motion of a stationary object are determined using... more Abstract: In one embodiment, the structure and motion of a stationary object are determined using two images and a linear velocity and linear acceleration of a camera. In another embodiment, the structure and motion of a stationary or moving object are determined using an image and linear and angular velocities of a camera.

Research paper thumbnail of Information flow based connectivity maintenance of a multi-agent system during formation control

Network connectivity is paramount for multi-agent systems with limited sensing and communication ... more Network connectivity is paramount for multi-agent systems with limited sensing and communication capabilities, since agents need to coordinate and communicate to make appropriate decisions in formation control. The goal in this work is to steer a group of agents to a desired configuration from any given initially connected graph in a decentralized manner, without partitioning the underlying network, and avoiding collision with other agents and moving obstacles. To maintain network connectivity, an information flow is proposed ...

Research paper thumbnail of Position-based visual servo control of leader-follower formation using image-based relative pose and relative velocity estimation

American Control Conference, …, Jun 10, 2009

A position-based visual servo control strategy is proposed for leader-follower formation control ... more A position-based visual servo control strategy is proposed for leader-follower formation control of unmanned ground vehicles (UGVs). The proposed control law only requires the knowledge of a single known length on the leader. The relative pose and the relative velocity of the leader are estimated with respect to the follower in the follower reference frame. The relative pose and the relative velocity are obtained using a geometric pose estimation technique and a nonlinear velocity estimation strategy, respectively. A ...

Research paper thumbnail of Nonlinear observer for structure estimation using a paracatadioptric camera

… Control Conference (ACC …, Jun 30, 2010

Estimation of the three-dimensional (3D) Euclidean coordinates of points on an object using two-d... more Estimation of the three-dimensional (3D) Euclidean coordinates of points on an object using two-dimensional (2D) image data is required by many robotics and surveillance applications. This paper develops a nonlinear observer to estimate relative Euclidean coordinates of an object viewed by a moving paracatadioptric camera with known motion. The observer exponentially estimates the structure of an object (ie Euclidean coordinates of different points on an object) provided sufficient observability conditions are satisfied. ...

Research paper thumbnail of Method and Apparatus for Nonlinear Dynamic Estimation of Feature Depth Using Calibrated Moving Cameras

Research paper thumbnail of Lyapunov-Based Nonlinear Estimation Methods With Applications to Machine Vision

ABSTRACT Recent advances in image-based information estimation has enabled the use of vision sens... more ABSTRACT Recent advances in image-based information estimation has enabled the use of vision sensor in many robotics and surveillance applications. The work in this dissertation, is focused on developing online techniques for image-based structure and motion (SaM) estimation. Since traditional batch methods are not useful for the online vision-based control tasks, observer-based approaches to the problem have been developed. Starting from the Kalman-filter for SaM problem by L. Matthies, many contributions to the observer approach for the SaM problem exist in literature. Various models are introduced in literature for SaM estimation but two models are prevalent, viz; a kinematic relative motion affine model with implicit outputs and a transformed nonlinear state model with the linear output equation. The existing SaM observers are designed for the case of a stationary object, requires full camera velocity information and cannot be used for certain camera motions. In this dissertation, new solutions to the SaM are presented using the transformed nonlinear state model which can be used for larger set of camera motions, does not require full camera velocity information, and are reduced-order. Solutions for the stationary as well as moving objects viewed by a moving camera are presented. In Chapter 3, a reduced order observer is developed to estimate the structure of a static object using a moving camera, where full camera velocity and linear acceleration are known. Chapter 4 focuses on the development of a reduced order observer for the SaM estimation of a stationary object when only a single camera linear velocity is known. In Chapter 5, an observer design is presented for a specific class of nonlinear systems where the output dynamics are affine in the unmeasurable state and the dynamics of the unmeasurable state are nonlinear. The method is applied to simultaneously estimate the structure and motion of a moving object seen by a moving camera. Another strategy to the observer design in the presence of an unmeasurable disturbance is an unknown input observer (UIO). Chapter 6 provides a solution to an UIO design for a general class of nonlinear systems and it's application to structure estimation of a moving object is shown in Chapter 7.

Research paper thumbnail of Gyro-aided image-based tracking using mutual information optimization and user inputs

2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2014

Research paper thumbnail of Dept. of Aerosp. Eng., Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA

Research paper thumbnail of Single Camera Structure and Motion Estimation

Lecture Notes in Control and Information Sciences, 2010

Two new continuous nonlinear observers are proposed for the problem of structure from motion (SfM... more Two new continuous nonlinear observers are proposed for the problem of structure from motion (SfM) and structure and motion (SaM) of a stationary object observed by a moving camera. The observer for SfM, where full velocity feedback is available, yields global exponential convergence of the states for the structure. The SaM observer requires only one of the linear velocities as a feedback and identifies the states asymptotically. The linear velocity is used to derive the scene scale information. The observer gain conditions are ...

Research paper thumbnail of Camera motion estimation for 3-D structure reconstruction of moving objects

2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2012

Abstract The method for camera motion estimation is proposed for the moving objects. Whereas the ... more Abstract The method for camera motion estimation is proposed for the moving objects. Whereas the estimation of the structure and motion (SaM) of the moving objects usually involves the constraints on the motion of the camera and the object, the moving camera velocities can be estimated in our work using the images of the moving object from the single camera without any constraint on the camera and object motion. To this end, the dynamics of the partially measurable state are arranged in such a way that the recursive ...

Research paper thumbnail of Inertial-Aided Vision-Based Localization and Mapping in a Riverine Environment with Reflection Measurements

AIAA Guidance, Navigation, and Control (GNC) Conference, 2013

Research paper thumbnail of Image moments for higher-level feature based navigation

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013

ABSTRACT This paper presents a novel vision-based localization and mapping algorithm using image ... more ABSTRACT This paper presents a novel vision-based localization and mapping algorithm using image moments of region features. The environment is represented using regions, such as planes and/or 3D objects instead of only a dense set of feature points. The regions can be uniquely defined using a small number of parameters; e.g., a plane can be completely characterized by normal vector and distance to a local coordinate frame attached to the plane. The variation of image moments of the regions in successive images can be related to the parameters of the regions. Instead of tracking a large number of feature points, variations of image moments of regions can be computed by tracking the segmented regions or a few feature points on the objects in successive images. A map represented by regions can be characterized using a minimal set of parameters. The problem is formulated as a nonlinear filtering problem. A new discrete-time nonlinear filter based on the state-dependent coefficient (SDC) form of nonlinear functions is presented. It is shown via Monte-Carlo simulations that the new nonlinear filter is more accurate and consistent than EKF by evaluating the root-mean squared error (RMSE) and normalized estimation error squared (NEES).

Research paper thumbnail of Observer design for stochastic nonlinear systems using contraction analysis

2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012

ABSTRACT This paper presents a new observer for Itô stochastic nonlinear systems with guaranteed ... more ABSTRACT This paper presents a new observer for Itô stochastic nonlinear systems with guaranteed stability. Contraction analysis is used to analyze incremental stability of the observer for an Itô stochastic nonlinear system. A bound on the mean squared distance between the trajectories of original dynamics and the observer dynamics is obtained as a function of contraction rate and maximum noise intensity. The observer design is based on non-unique state-dependent coefficient (SDC) forms which parametrize the nonlinearity in an extended linear form. In this paper, a convex combination of several parametrizations is used. An optimization problem with state-dependent linear matrix inequality (SDLMI) constraints is formulated to select the free parameters of the convex combination for achieving faster convergence and robustness against disturbances. Moreover, the L2 norm of the disturbance and noise to the estimation error is shown to be finite. The present algorithm shows improved performance in comparison to the extended Kalman filter (EKF) and the state-dependent differential Riccati equation (SDDRE) filter in simulation.

Research paper thumbnail of Globally exponentially convergent observer for vision-based range estimation

2010 IEEE International Symposium on Intelligent Control, 2010

Research paper thumbnail of Nonlinear estimation of gait kinematics during functional electrical stimulation and orthosis-based walking

2014 American Control Conference, 2014

ABSTRACT This paper presents a nonlinear estimation algorithm which utilizes a low-degree of free... more ABSTRACT This paper presents a nonlinear estimation algorithm which utilizes a low-degree of freedom model of functional electrical stimulation (FES) and orthosis-based walking to estimate lower-limb angles. The estimated lower limb angles can be used to decide when the FES signal should be applied to the leg during the different phases of walking. To this end, we use measurements from inertial measurement units (IMUs) to estimate the lower limb segment angles. A state-dependent coefficient (SDC)-based nonlinear estimator is developed to estimate the lower limb angles. The nonlinear estimator is robust to uncertainties in the motion modeling and sensor noise/bias from the IMUs. A comparison with extended Kalman (EKF)-like filter shows improved performance of the estimator in simulation studies.

Research paper thumbnail of Information fusion in human-robot collaboration using neural network representation

2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2014

Research paper thumbnail of Globally exponentially stable observer for vision-based range estimation

Research paper thumbnail of Single Camera Structure and Motion

IEEE Transactions on Automatic Control, 2012

Abstract A reduced order nonlinear observer is proposed for the problem of “structure and motion ... more Abstract A reduced order nonlinear observer is proposed for the problem of “structure and motion (SaM)” estimation of a stationary object observed by a moving calibrated camera. In comparison to existing work which requires some knowledge of the Euclidean geometry of an observed object or full knowledge of the camera motion, the developed reduced order observer only requires one camera linear velocity and corresponding acceleration to asymptotically identify the Euclidean coordinates of the feature points attached to an ...

Research paper thumbnail of Network Connectivity Preserving Formation Stabilization and Obstacle Avoidance via a Decentralized Controller

IEEE Transactions on Automatic Control, 2000

Research paper thumbnail of Saturated control of an uncertain nonlinear system with input delay

Automatica, 2013

ABSTRACT This paper examines saturated control of a general class of uncertain nonlinear systems ... more ABSTRACT This paper examines saturated control of a general class of uncertain nonlinear systems with time-delayed actuation and additive bounded disturbances. The bound on the control is known a priori and can be adjusted by changing the feedback gains. A Lyapunov-based stability analysis utilizing Lyapunov–Krasovskii (LK) functionals is provided to prove uniformly ultimately bounded tracking despite uncertainties in the dynamics. A numerical example is presented to demonstrate the performance of the controller.

Research paper thumbnail of Systems and Methods for Estimating the Structure and Motion of an Object

Abstract: In one embodiment, the structure and motion of a stationary object are determined using... more Abstract: In one embodiment, the structure and motion of a stationary object are determined using two images and a linear velocity and linear acceleration of a camera. In another embodiment, the structure and motion of a stationary or moving object are determined using an image and linear and angular velocities of a camera.

Research paper thumbnail of Information flow based connectivity maintenance of a multi-agent system during formation control

Network connectivity is paramount for multi-agent systems with limited sensing and communication ... more Network connectivity is paramount for multi-agent systems with limited sensing and communication capabilities, since agents need to coordinate and communicate to make appropriate decisions in formation control. The goal in this work is to steer a group of agents to a desired configuration from any given initially connected graph in a decentralized manner, without partitioning the underlying network, and avoiding collision with other agents and moving obstacles. To maintain network connectivity, an information flow is proposed ...

Research paper thumbnail of Position-based visual servo control of leader-follower formation using image-based relative pose and relative velocity estimation

American Control Conference, …, Jun 10, 2009

A position-based visual servo control strategy is proposed for leader-follower formation control ... more A position-based visual servo control strategy is proposed for leader-follower formation control of unmanned ground vehicles (UGVs). The proposed control law only requires the knowledge of a single known length on the leader. The relative pose and the relative velocity of the leader are estimated with respect to the follower in the follower reference frame. The relative pose and the relative velocity are obtained using a geometric pose estimation technique and a nonlinear velocity estimation strategy, respectively. A ...

Research paper thumbnail of Nonlinear observer for structure estimation using a paracatadioptric camera

… Control Conference (ACC …, Jun 30, 2010

Estimation of the three-dimensional (3D) Euclidean coordinates of points on an object using two-d... more Estimation of the three-dimensional (3D) Euclidean coordinates of points on an object using two-dimensional (2D) image data is required by many robotics and surveillance applications. This paper develops a nonlinear observer to estimate relative Euclidean coordinates of an object viewed by a moving paracatadioptric camera with known motion. The observer exponentially estimates the structure of an object (ie Euclidean coordinates of different points on an object) provided sufficient observability conditions are satisfied. ...