<p>Indoor navigation could be used in many applications to enhance performance in</p><p>its specific area. Anything from serious life critical tasks like aiding firefighters or</p><p>coordinating military attacks to more simple every day use like finding a desired</p><p>shop in a large supermarket could be considered. Smartphones of today introduce</p><p>an interesting platform with capabilities like existing, more clumsy, indoor</p><p>navigation systems. The iPhone 3GS is a powerful smartphone that lets the programmer</p><p>use its hardware in an efficient and easy way. The iPhone 3GS has a</p><p>3-axis accelerometer, a 3-axis magnetometer and hardware accelerated image rendering</p><p>which is used in this thesis to track the user on an indoor map. A particle</p><p>filter is used to track the position of the user. The implementation shows how</p><p>many particles the iPhone will be able to handle and update in real time without</p><p>lag in the application.</p>

A mobile robot must be able to determine its own position in the world. To support truly autonomous navigation, we present a system that builds and maintains its own models of world locations and uses these models to recognize its world position from stereo vision input. The system is designed to be robust with respect to input errors and to respond to a gradually changing world by updating the world location models. We present results from tests of the system that demonstrate its reliability. The model builder and recognition system fit into a planned world modeling system that we describe.

Representing the world in terms of visible surfaces and the freespacesexisting between these surfaces and the viewer is an important problemsin robotics. Recently, researchers have proposed using the 3DsDelaunay Tessellation for representing 3D stereo vision data and thesfreespace determined therefrom. We discuss problems with using thes3D Delaunay Tessellation as the basis of the representation andspropose an alternative representation that we are currentlysinvestigating. This new representation is appropriate for planningsmobile robot navigation and promises to be robust when using stereosdata that has errors and uncertainty.

We propose a scheme for indoor place identification based on the recognition of global scene views. Scene views are encoded using a holistic representation that provides low-resolution spatial and spectral information. The holistic nature of the representation dispenses with the need to rely on specific objects or local landmarks and also renders it robust against variations in object configurations. We demonstrate the scheme on the problem of recognizing scenes in video sequences captured while walking through an office environment. We develop a method for distinguishing between 'diagnostic' and 'generic' views and also evaluate changes in system performances as a function of the amount of training data available and the complexity of the representation.

Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, September 1997. / Thesis Advisor(s): Murali Tummala. "September 1997." Includes bibliographical references (p. 61-62). Also available in print.

Thesis (Ph.D.)--Ohio University, June, 2006. / Title from PDF t.p. Includes bibliographical references (p. 104-112)

The ability to navigate in an unknown environment is an enabler for truly utonomous systems. Such a system must be aware of its relative position to the surroundings using sensor measurements. It is instrumental that these measurements are monitored for disturbances and faults. Having correct measurements, the challenging problem for a robot is to estimate its own position and simultaneously build a map of the environment. This problem is referred to as the Simultaneous Localization and Mapping (SLAM) problem. This thesis studies several topics related to SLAM, on-board sensor processing, exploration and disturbance detection. The particle filter (PF) solution to the SLAM problem is commonly referred to as FastSLAM and has been used extensively for ground robot applications. Having more complex vehicle models using for example flying robots extends the state dimension of the vehicle model and makes the existing solution computationally infeasible. The factorization of the problem made in this thesis allows for a computationally tractable solution. Disturbance detection for magnetometers and detection of spurious features in image sensors must be done before these sensor measurements can be used for estimation. Disturbance detection based on comparing a batch of data with a model of the system using the generalized likelihood ratio test is considered. There are two approaches to this problem. One is based on the traditional parity space method, where the influence of the initial state is removed by projection, and the other on combining prior information with data in the batch. An efficient parameterization of incipient faults is given which is shown to improve the results considerably. Another common situation in robotics is to have different sampling rates of the sensors. More complex sensors such as cameras often have slower update rate than accelerometers and gyroscopes. An algorithm for this situation is derived for a class of models with linear Gaussian dynamic model and sensors with different sampling rates, one slow with a nonlinear and/or non-Gaussian measurement relation and one fast with a linear Gaussian measurement relation. For this case, the Kalman filter is used to process the information from the fast sensor and the information from the slow sensor is processed using the PF. The problem formulation covers the important special case of fast dynamics and one slow sensor, which appears in many navigation and tracking problems. Vision based target tracking is another important estimation problem in robotics. Distributed exploration with multi-aircraft flight experiments has demonstrated localization of a stationary target with estimate covariance on the order of meters. Grid-based estimation as well as the PF have been examined. / The third article in this thesis is included with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Linköping University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this material, you agree to all provisions of the copyright laws protecting it.Please be advised that wherever a copyright notice from another organization is displayed beneath a figure, a photo, a videotape or a Powerpoint presentation, you must get permission from that organization, as IEEE would not be the copyright holder.

An autonomous machine is a machine that can navigate through its environment without human interactions. These machines use sensors to sense the environment and have computing abilities for receiving and interpreting the sensory data as well as for controlling their displacement. At the University of Saskatchewan (Saskatoon, Canada), a low level autonomous machine was developed. This low level machine was the sensor system for an autonomous machine. The machine was capable of sensing the environment and carrying out actions based on commands sent to it. This machine provided a sensing and control layer, but the path planning (decision making) part of the autonomous machine was not developed.<p>This autonomous machine was developed on a Case IH DX 34H tractor with the purpose of providing a machine for testing software and sensors in a true agricultural environment. The tractor was equipped with sensors capable of sensing the speed and heading of the tractor. A control architecture was developed that received input commands from a human or computer in the form of a target heading and speed. The control architecture then adjusted controls on the tractor to make the tractor reach and maintain the target heading and speed until a new command was provided. The tractor was capable of being used in all kinds of weather, although some minor issues arose when testing in rain and snow. The sensor platform developed was found to be insufficient for proper control. The control structure appeared to work correctly, but was hindered by the poor sensor platform performance.

The thesis covers data fusion for aircraft navigation systems in distributed sensor systems. Data fusion methodologies are developed for the design, development, analysis and simulation of multisensor aircraft navigation systems. The problems of sensor failure detection and isolation (FDI), distributed data fusion algorithms and inertial state integrity monitoring in inertial network systems are studied. Various existing integrated navigation systems and Kalman filter architectures are reviewed and a new generalised multisensor data fusion model is presented for the design and development of multisensor navigation systems. Normalised navigation algorithms are described for data fusion filter design of inertial network systems. A normalised measurement model of skewed redundant inertial measurement units (SRIMU) is presented and performance criteria are developed to evaluate optimal configurations of SRIMUs in terms of the measurement accuracy and FDI capability. Novel sensor error compensation filters are designed for the correction of SRIMU measurement errors. Generalised likelihood ratio test (GLRT) methods are improved to detect various failure modes, including short time and sequential moving-window GLRT algorithms. State-identical and state-associated fusion algorithms are developed for two forms of distributed sensor network systems. In particular, innovative inertial network sensing models and inertial network fusion algorithms are developed to provide estimates of inertial vector states and similar node states. Fusion filter-based integrity monitoring algorithms are also presented to detect network sensor failures and to examine the consistency of node state estimates in the inertial network system. The FDI and data fusion algorithms developed in this thesis are tested and their performance is evaluated using a multisensor software simulation system developed during this study programme. The moving-window GLRT algorithms for optimal SRIMU configurations are shown to perform well and are also able to detect jump and drift failures in an inertial network system. It is concluded that the inertial network fusion algorithms could be used in a low-cost inertial network system and are capable of correctly estimating the inertial vector states and the node states.

Indoor navigation could be used in many applications to enhance performance in its specific area. Anything from serious life critical tasks like aiding firefighters or coordinating military attacks to more simple every day use like finding a desired shop in a large supermarket could be considered. Smartphones of today introduce an interesting platform with capabilities like existing, more clumsy, indoor navigation systems. The iPhone 3GS is a powerful smartphone that lets the programmer use its hardware in an efficient and easy way. The iPhone 3GS has a 3-axis accelerometer, a 3-axis magnetometer and hardware accelerated image rendering which is used in this thesis to track the user on an indoor map. A particle filter is used to track the position of the user. The implementation shows how many particles the iPhone will be able to handle and update in real time without lag in the application.