Online Discrimination of Nonlinear Dynamics with Switching Differential Equations

How to recognise whether an observed person walks or runs? We consider a dynamic environment where observations (e.g. the posture of a person) are caused by different dynamic processes (walking or running) which are active one at a time and which may transition from one to another at any time. For this setup, switching dynamic models have been suggested previously, mostly, for linear and nonlinear dynamics in discrete time. Motivated by basic principles of computations in the brain (dynamic, internal models) we suggest a model for switching nonlinear differential equations. The switching process in the model is implemented by a Hopfield network and we use parametric dynamic movement primitives to represent arbitrary rhythmic motions. The model generates observed dynamics by linearly interpolating the primitives weighted by the switching variables and it is constructed such that standard filtering algorithms can be applied. In two experiments with synthetic planar motion and a human motion capture data set we show that inference with the unscented Kalman filter can successfully discriminate several dynamic processes online

Theory, Design, and Implementation of Landmark Promotion Cooperative Simultaneous Localization and Mapping

Simultaneous Localization and Mapping (SLAM) is a challenging problem in practice, the use of multiple robots and inexpensive sensors poses even more demands on the designer. Cooperative SLAM poses specific challenges in the areas of computational efficiency, software/network performance, and robustness to errors. New methods in image processing, recursive filtering, and SLAM have been developed to implement practical algorithms for cooperative SLAM on a set of inexpensive robots. The Consolidated Unscented Mixed Recursive Filter (CUMRF) is designed to handle non-linear systems with non-Gaussian noise. This is accomplished using the Unscented Transform combined with Gaussian Mixture Models. The Robust Kalman Filter is an extension of the Kalman Filter algorithm that improves the ability to remove erroneous observations using Principal Component Analysis (PCA) and the X84 outlier rejection rule. Forgetful SLAM is a local SLAM technique that runs in nearly constant time relative to the number of visible landmarks and improves poor performing sensors through sensor fusion and outlier rejection. Forgetful SLAM correlates all measured observations, but stops the state from growing over time. Hierarchical Active Ripple SLAM (HAR-SLAM) is a new SLAM architecture that breaks the traditional state space of SLAM into a chain of smaller state spaces, allowing multiple robots, multiple sensors, and multiple updates to occur in linear time with linear storage with respect to the number of robots, landmarks, and robots poses. This dissertation presents explicit methods for closing-the-loop, joining multiple robots, and active updates. Landmark Promotion SLAM is a hierarchy of new SLAM methods, using the Robust Kalman Filter, Forgetful SLAM, and HAR-SLAM. Practical aspects of SLAM are a focus of this dissertation. LK-SURF is a new image processing technique that combines Lucas-Kanade feature tracking with Speeded-Up Robust Features to perform spatial and temporal tracking. Typical stereo correspondence techniques fail at providing descriptors for features, or fail at temporal tracking. Several calibration and modeling techniques are also covered, including calibrating stereo cameras, aligning stereo cameras to an inertial system, and making neural net system models. These methods are important to improve the quality of the data and images acquired for the SLAM process

Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion

Motion tracking based on commercial inertial measurements units (IMUs) has been widely studied in the latter years as it is a cost-effective enabling technology for those applications in which motion tracking based on optical technologies is unsuitable. This measurement method has a high impact in human performance assessment and human-robot interaction. IMU motion tracking systems are indeed self-contained and wearable, allowing for long-lasting tracking of the user motion in situated environments. After a survey on IMU-based human tracking, five techniques for motion reconstruction were selected and compared to reconstruct a human arm motion. IMU based estimation was matched against motion tracking based on the Vicon marker-based motion tracking system considered as ground truth. Results show that all but one of the selected models perform similarly (about 35 mm average position estimation error)

Particle Filters for Colour-Based Face Tracking Under Varying Illumination

Automatic human face tracking is the basis of robotic and active vision systems used for facial feature analysis, automatic surveillance, video conferencing, intelligent transportation, human-computer interaction and many other applications. Superior human face tracking will allow future safety surveillance systems which monitor drowsy drivers, or patients and elderly people at the risk of seizure or sudden falls and will perform with lower risk of failure in unexpected situations. This area has actively been researched in the current literature in an attempt to make automatic face trackers more stable in challenging real-world environments. To detect faces in video sequences, features like colour, texture, intensity, shape or motion is used. Among these feature colour has been the most popular, because of its insensitivity to orientation and size changes and fast process-ability. The challenge of colour-based face trackers, however, has been dealing with the instability of trackers in case of colour changes due to the drastic variation in environmental illumination. Probabilistic tracking and the employment of particle filters as powerful Bayesian stochastic estimators, on the other hand, is increasing in the visual tracking field thanks to their ability to handle multi-modal distributions in cluttered scenes. Traditional particle filters utilize transition prior as importance sampling function, but this can result in poor posterior sampling. The objective of this research is to investigate and propose stable face tracker capable of dealing with challenges like rapid and random motion of head, scale changes when people are moving closer or further from the camera, motion of multiple people with close skin tones in the vicinity of the model person, presence of clutter and occlusion of face. The main focus has been on investigating an efficient method to address the sensitivity of the colour-based trackers in case of gradual or drastic illumination variations. The particle filter is used to overcome the instability of face trackers due to nonlinear and random head motions. To increase the traditional particle filter\u27s sampling efficiency an improved version of the particle filter is introduced that considers the latest measurements. This improved particle filter employs a new colour-based bottom-up approach that leads particles to generate an effective proposal distribution. The colour-based bottom-up approach is a classification technique for fast skin colour segmentation. This method is independent to distribution shape and does not require excessive memory storage or exhaustive prior training. Finally, to address the adaptability of the colour-based face tracker to illumination changes, an original likelihood model is proposed based of spatial rank information that considers both the illumination invariant colour ordering of a face\u27s pixels in an image or video frame and the spatial interaction between them. The original contribution of this work lies in the unique mixture of existing and proposed components to improve colour-base recognition and tracking of faces in complex scenes, especially where drastic illumination changes occur. Experimental results of the final version of the proposed face tracker, which combines the methods developed, are provided in the last chapter of this manuscript

Distributed Object Tracking Using a Cluster-Based Kalman Filter in Wireless Camera Networks

Local data aggregation is an effective means to save sensor node energy and prolong the lifespan of wireless sensor networks. However, when a sensor network is used to track moving objects, the task of local data aggregation in the network presents a new set of challenges, such as the necessity to estimate, usually in real time, the constantly changing state of the target based on information acquired by the nodes at different time instants. To address these issues, we propose a distributed object tracking system which employs a cluster-based Kalman filter in a network of wireless cameras. When a target is detected, cameras that can observe the same target interact with one another to form a cluster and elect a cluster head. Local measurements of the target acquired by members of the cluster are sent to the cluster head, which then estimates the target position via Kalman filtering and periodically transmits this information to a base station. The underlying clustering protocol allows the current state and uncertainty of the target position to be easily handed off among clusters as the object is being tracked. This allows Kalman filter-based object tracking to be carried out in a distributed manner. An extended Kalman filter is necessary since measurements acquired by the cameras are related to the actual position of the target by nonlinear transformations. In addition, in order to take into consideration the time uncertainty in the measurements acquired by the different cameras, it is necessary to introduce nonlinearity in the system dynamics. Our object tracking protocol requires the transmission of significantly fewer messages than a centralized tracker that naively transmits all of the local measurements to the base station. It is also more accurate than a decentralized tracker that employs linear interpolation for local data aggregation. Besides, the protocol is able to perform real-time estimation because our implementation takes into consideration the sparsit- - y of the matrices involved in the problem. The experimental results show that our distributed object tracking protocol is able to achieve tracking accuracy comparable to the centralized tracking method, while requiring a significantly smaller number of message transmissions in the network

Industrial applications of the Kalman filter:a review

International audienc

Stochastic Modeling for Mobile Manipulators

Mobile manipulators are valuable and highly desired in many fields, especially in industrial environments. However, determining the end effector position has been challenging for scenarios where the base moves at the same time that the arm follows commands to perform specific tasks. Earlier works have attempted to dynamically evaluate the problem of positioning error for mobile manipulators, but there is still room for further improvement. In this thesis, we devise a dynamical model that leverages stochastic search strategies for mobile manipulators. More specifically, we develop a dynamic model that estimates the position of the robot using an Unscented Kalman filter. Simulations using the Robot Operating System (ROS) and Gazebo were carried out to evaluate our model. Our results for the stochastic method show that it outperforms a deterministic approach (spiral search) under specific Kalman filter covariances of the process and observation noises. Compared to the state of the art, our proposed approach is more robust and efficient, proving to work under different arrangement scenarios with significant better performance