Development of a robotic mobile mapping system by vision-aided inertial navigation:a geomatics approach

Vision-based inertial-aided navigation is gaining ground due to its many potential applications. In previous decades, the integration of vision and inertial sensors was monopolised by the defence industry due to its complexity and unrealistic economic burden. After the technology advancement, high-quality hardware and computing power became reachable for the investigation and realisation of various applications. In this thesis, a mapping system by vision-aided inertial navigation was developed for areas where GNSS signals are unreachable, for example, indoors, tunnels, city canyons, forests, etc. In this framework, a methodology on the integration of vision and inertial sensors was presented, analysed and tested when the only available information at the beginning is a number of features with known location/coordinates (with no GNSS signals accessibility), thus employing the method of "SLAM: Simultaneous Localisation And Mapping". SLAM is a term used in the robotics community to describe the problem of mapping the environment and at the same time using this map to determine (or to help in determining) the location of the mapping device. In addition to this, a link between the robotics and geomatics community was established where briefly the similarities and differences were outlined in terms of handling the navigation and mapping problem. Albeit many differences, the goal is common: developing a "navigation and mapping system" that is not bounded to the limits imposed by the used sensors. Classically, terrestrial robotics SLAM is approached using LASER scanners to locate the robot relative to a structured environment and to map this environment at the same time. However, outdoors robotics SLAM is not feasible with LASER scanners alone due to the environment's roughness and absence of simple geometric features. Recently in the robotics community, the use of visual methods, integrated with inertial sensors, has gained an interest. These visual methods rely on one or more cameras (or video) and make use of a single Kalman Filter with a state vector containing the map and the robot coordinates. This concept introduces high non-linearity and complications to the filter, which then needs to run at high rates (more than 20 Hz) with simplified navigation and mapping models. In this study, SLAM is developed using the Geomatics Engineering approach. Two filters are used in parallel: the Least-Squares Adjustment (LSA) for feature coordinates determination and the Kalman Filter (KF) for navigation correction. For this, a mobile mapping system (independent of GPS) is introduced by employing two CCD cameras (one metre apart) and one IMU. Conceptually, the outputs of the LSA photogrammetric resection (position and orientation) are used as the external measurements for the inertial KF. The filtered position and orientation are subsequently employed in the Photogrammetric intersection to map the surrounding features that are used as control points for the resection in the next epoch. In this manner, the KF takes the form of a navigation only filter, with a state vector containing the corrections to the navigation parameters. This way, the mapping and localisation can be updated at low rates (1 to 2 Hz) and use more complete modelling. Results show that this method is feasible with limitation induced from the quality of the images and the number of used features. Although simulation showed that (depending on the image geometry) determining the features' coordinates with an accuracy of 5-10 cm for objects at distances of up to 10 metres is possible, in practice this is not achieved with the employed hardware and pixel measurement techniques. Navigational accuracies depend as well on the quality of the images and the number and accuracy of the points used in the resection. While more than 25 points are needed to achieve centimetre accuracy from resection, they have to be within a distance of 10 metres from the cameras; otherwise, the resulting resection output will be of insufficient accuracy and further integration quality deteriorates. The initial conditions highly affect SLAM performance; these are the method of IMU initialisation and the a-priori assumptions on error distribution. The geometry of the system will furthermore have a consequence on possible applications. To conclude, the development consisted in establishing a mathematical framework, as well as implementing methods and algorithms for a novel integration methodology between vision and inertial sensors. The implementation and validation of the software have presented the main challenges, and it can be considered the first of a kind where all components were developed from scratch, with no pre-existing modules. Finally, simulations and practical tests were carried out, from which initial conclusions and recommendations were drawn to build upon. It is the author's hope that this work will stimulate others to investigate further this interesting problem taking into account the conclusions and recommendations sketched herein

Generic Multisensor Integration Strategy and Innovative Error Analysis for Integrated Navigation

A modern multisensor integrated navigation system applied in most of civilian applications typically consists of GNSS (Global Navigation Satellite System) receivers, IMUs (Inertial Measurement Unit), and/or other sensors, e.g., odometers and cameras. With the increasing availabilities of low-cost sensors, more research and development activities aim to build a cost-effective system without sacrificing navigational performance. Three principal contributions of this dissertation are as follows: i) A multisensor kinematic positioning and navigation system built on Linux Operating System (OS) with Real Time Application Interface (RTAI), York University Multisensor Integrated System (YUMIS), was designed and realized to integrate GNSS receivers, IMUs, and cameras. YUMIS sets a good example of a low-cost yet high-performance multisensor inertial navigation system and lays the ground work in a practical and economic way for the personnel training in following academic researches. ii) A generic multisensor integration strategy (GMIS) was proposed, which features a) the core system model is developed upon the kinematics of a rigid body; b) all sensor measurements are taken as raw measurement in Kalman filter without differentiation. The essential competitive advantages of GMIS over the conventional error-state based strategies are: 1) the influences of the IMU measurement noises on the final navigation solutions are effectively mitigated because of the increased measurement redundancy upon the angular rate and acceleration of a rigid body; 2) The state and measurement vectors in the estimator with GMIS can be easily expanded to fuse multiple inertial sensors and all other types of measurements, e.g., delta positions; 3) one can directly perform error analysis upon both raw sensor data (measurement noise analysis) and virtual zero-mean process noise measurements (process noise analysis) through the corresponding measurement residuals of the individual measurements and the process noise measurements. iii) The a posteriori variance component estimation (VCE) was innovatively accomplished as an advanced analytical tool in the extended Kalman Filter employed by the GMIS, which makes possible the error analysis of the raw IMU measurements for the very first time, together with the individual independent components in the process noise vector

Privacy Protection, At What Cost? Exploring the Regulatory Resistance to Data Technology in Auto Insurance

Regulatory and sociological resistance to new market-driven technologies, particularly to those that rely on collection and analysis of personal data, is prevalent even in cases where the technology creates large social value and saves lives. This article is a case study of such tragic technology resistance, focusing on tracking devices in cars which allow auto insurers to monitor how policyholders drive and adjust the premiums accordingly. Growing empirical work reveals that such “usage-based insurance” induces safer driving, reducing fatal accidents by almost one third, and resulting in more affordable and fair premiums. Yet, California prohibits this technology and other states limit its effectiveness, largely in the interest of privacy protection. The article evaluates the justifications fueling the restrictive regulation vis-à-vis the loss of lives resulting from this regulation. It concludes that the social benefits of the tracking technology dramatically outweigh the privacy and related costs

Görsel-ataletsel duyaç tümleştirme kullanılarak şehirlerde 3b modelleme.

In this dissertation, a real-time, autonomous and geo-registered approach is presented to tackle the large scale 3D urban modeling problem using a camera and inertial sensors. The proposed approach exploits the special structures of urban areas and visual-inertial sensor fusion. The buildings in urban areas are assumed to have planar facades that are perpendicular to the local level. A sparse 3D point cloud of the imaged scene is obtained from visual feature matches using camera poses estimates, and planar patches are obtained by an iterative Hough Transform on the 2D projection of the sparse 3D point cloud in the direction of gravity. The result is a compact and dense depth map of the building facades in terms of planar patches. The plane extraction is performed on sequential frames and a complete model is obtained by plane fusion. Inertial sensor integration helps to improve camera pose estimation, 3D reconstruction and planar modeling stages. For camera pose estimation, the visual measurements are integrated with the inertial sensors by means of an indirect feedback Kalman filter. This integration helps to get reliable and geo-referenced camera pose estimates in the absence of GPS. The inertial sensors are also used to filter out spurious visual feature matches in the 3D reconstruction stage, find the direction of gravity in plane search stage, and eliminate out of scope objects from the model using elevation data. The visual-inertial sensor fusion and urban heuristics utilization are shown to outperform the classical approaches for large scale urban modeling in terms of consistency and real-time applicability.Ph.D. - Doctoral Progra

Overcoming the challenges of low-cost inertial navigation

Inertial navigation is always available as a base for multisensor navigation systems on, because it requires no external signals. However, measurement errors persist and grow with time so accurate calibration is crucial. Large systematic errors are present in the micro-electro-mechanical sensors (MEMS) whose low cost brings inertial navigation to many new applications. Using factory-calibrated MEMS another navigation technology can calibrate these errors with in-run estimation using a Kalman filter (KF). However, the raw systematic errors of low-cost MEMS are often too large for stable performance. This thesis contributes to knowledge in three areas. First, it takes a simple GNSS-inertial KF and examines the levels of the various systematic errors which cause the integration to fail. This allows the user to know how well calibrated the sensors need to be to use in-run calibration. Second, the thesis examines how the end-user could conduct a calibration: it analyses one method in detail showing how imperfections in the procedure affect the results and comparing calculation methods. This is important as frequently calibration methods are only validated by demonstrating consistent results for one particular sensor. These two are primarily accomplished using statistical Monte Carlo simulations. Third, techniques are examined by which an array of inertial sensors could be used to produce an output which is better than the simple array average. This includes methods that reduce the array’s sensitivity to environmental conditions, this is important because the sensors’ calibration typically depends strongly on temperature. Also included in the thesis are descriptions of experimental hardware and experiments which have been carried to support and unify the other parts of the thesis. Overall, this thesis’ contributions will help make low-cost inertial navigation systems more accurate and will allow system designers to concentrate effort where it will make the most difference

Journal of the Senate, session of 1971.

Titles and imprints vary; Some volumes include miscellaneous state documents and reports; Rules of the Senat

Journal of the house, session of 1971.

Titles and imprints vary; Some volumes include miscellaneous state documents and reports; Rules of the House of Representative

Business Law and the Legal Environment

https://digitalcommons.sacredheart.edu/opentexts/1000/thumbnail.jp