This thesis focuses on the theory and implementation of visual navigation techniques for Autonomous Air Vehicles in outdoor environments. The target of this study is to fuse and cooperatively develop an incremental map for multiple air vehicles under the application of Simultaneous Location and Mapping (SLAM). Without loss of generality, two unmanned air vehicles (UAVs) are investigated for the generation of ground maps from current and a priori data. Each individual UAV is equipped with inertial navigation systems and external sensitive elements which can provide the possible mixture of visible, thermal infrared (IR) image sensors, with a special emphasis on the stereo digital cameras. The corresponding stereopsis is able to provide the crucial three-dimensional (3-D) measurements. Therefore, the visual aerial navigation problems tacked here are interpreted as stereo vision based SLAM (vSLAM) for both single and multiple UAVs applications. To begin with, the investigation is devoted to the methodologies of feature extraction. Potential landmarks are selected from airborne camera images as distinctive points identified in the images are the prerequisite for the rest. Feasible feature extraction algorithms have large influence over feature matching/association in 3-D mapping. To this end, effective variants of scale-invariant feature transform (SIFT) algorithms are employed to conduct comprehensive experiments on feature extraction for both visible and infrared aerial images. As the UAV is quite often in an uncertain location within complex and cluttered environments, dense and blurred images are practically inevitable. Thus, it becomes a challenge to find feature correspondences, which involves feature matching between 1st and 2nd image in the same frame, and data association of mapped landmarks and camera measurements. A number of tests with different techniques are conducted by incorporating the idea of graph theory and graph matching. The novel approaches, which could be tagged as classification and hypergraph transformation (HGTM) based respectively, have been proposed to solve the data association in stereo vision based navigation. These strategies are then utilised and investigated for UAV application within SLAM so as to achieve robust matching/association in highly cluttered environments. The unknown nonlinearities in the system model, including noise would introduce undesirable INS drift and errors. Therefore, appropriate appraisals on the pros and cons of various potential data filtering algorithms to resolve this issue are undertaken in order to meet the specific requirements of the applications. These filters within visual SLAM were put under investigation for data filtering and fusion of both single and cooperative navigation. Hence updated information required for construction and maintenance of a globally consistent map can be provided by using a suitable algorithm with the compromise between computational accuracy and intensity imposed by the increasing map size. The research provides an overview of the feasible filters, such as extended Kalman Filter, extended Information Filter, unscented Kalman Filter and unscented H Infinity Filter. As visual intuition always plays an important role for humans to recognise objects, research on 3-D mapping in textures is conducted in order to fulfil the purpose of both statistical and visual analysis for aerial navigation. Various techniques are proposed to smooth texture and minimise mosaicing errors during the reconstruction of 3-D textured maps with vSLAM for UAVs. Finally, with covariance intersection (CI) techniques adopted on multiple sensors, various cooperative and data fusion strategies are introduced for the distributed and decentralised UAVs for Cooperative vSLAM (C-vSLAM). Together with the complex structure of high nonlinear system models that reside in cooperative platforms, the robustness and accuracy of the estimations in collaborative mapping and location are achieved through HGTM association and communication strategies. Data fusion among UAVs and estimation for visual navigation via SLAM were impressively verified and validated in conditions of both simulation and real data sets. / © Cranfield University, 2013

This thesis focuses on the theory and implementation of visual navigation techniques for Autonomous Air Vehicles in outdoor environments. The target of this study is to fuse and cooperatively develop an incremental map for multiple air vehicles under the application of Simultaneous Location and Mapping (SLAM). Without loss of generality, two unmanned air vehicles (UAVs) are investigated for the generation of ground maps from current and a priori data. Each individual UAV is equipped with inertial navigation systems and external sensitive elements which can provide the possible mixture of visible, thermal infrared (IR) image sensors, with a special emphasis on the stereo digital cameras. The corresponding stereopsis is able to provide the crucial three-dimensional (3-D) measurements. Therefore, the visual aerial navigation problems tacked here are interpreted as stereo vision based SLAM (vSLAM) for both single and multiple UAVs applications. To begin with, the investigation is devoted to the methodologies of feature extraction. Potential landmarks are selected from airborne camera images as distinctive points identified in the images are the prerequisite for the rest. Feasible feature extraction algorithms have large influence over feature matching/association in 3-D mapping. To this end, effective variants of scale-invariant feature transform (SIFT) algorithms are employed to conduct comprehensive experiments on feature extraction for both visible and infrared aerial images. As the UAV is quite often in an uncertain location within complex and cluttered environments, dense and blurred images are practically inevitable. Thus, it becomes a challenge to find feature correspondences, which involves feature matching between 1st and 2nd image in the same frame, and data association of mapped landmarks and camera measurements. A number of tests with different techniques are conducted by incorporating the idea of graph theory and graph matching. The novel approaches, which could be tagged as classification and hypergraph transformation (HGTM) based respectively, have been proposed to solve the data association in stereo vision based navigation. These strategies are then utilised and investigated for UAV application within SLAM so as to achieve robust matching/association in highly cluttered environments. The unknown nonlinearities in the system model, including noise would introduce undesirable INS drift and errors. Therefore, appropriate appraisals on the pros and cons of various potential data filtering algorithms to resolve this issue are undertaken in order to meet the specific requirements of the applications. These filters within visual SLAM were put under investigation for data filtering and fusion of both single and cooperative navigation. Hence updated information required for construction and maintenance of a globally consistent map can be provided by using a suitable algorithm with the compromise between computational accuracy and intensity imposed by the increasing map size. The research provides an overview of the feasible filters, such as extended Kalman Filter, extended Information Filter, unscented Kalman Filter and unscented H Infinity Filter. As visual intuition always plays an important role for humans to recognise objects, research on 3-D mapping in textures is conducted in order to fulfil the purpose of both statistical and visual analysis for aerial navigation. Various techniques are proposed to smooth texture and minimise mosaicing errors during the reconstruction of 3-D textured maps with vSLAM for UAVs. Finally, with covariance intersection (CI) techniques adopted on multiple sensors, various cooperative and data fusion strategies are introduced for the distributed and decentralised UAVs for Cooperative vSLAM (C-vSLAM). Together with the complex structure of high nonlinear system models that reside in cooperative platforms, the robustness and accuracy of the estimations in collaborative mapping and location are achieved through HGTM association and communication strategies. Data fusion among UAVs and estimation for visual navigation via SLAM were impressively verified and validated in conditions of both simulation and real data sets.

Positioning and navigation systems play an important role in our daily life, but now most of positioning systems were confined in outdoor environments, most of which were used on transportation. Therefore, the goal of this thesis is to develop a Pedestrian Dead Reckoning System (PDRS), which can not only be used to solve a problem of GPS out-of-lock, but also be used in the field of indoor positioning. In dangerous environments, such as the scene of a fire, when the rescue personnel have an accident on himself or discover a wounded who need to be salvaged, if the rescue personnel who has configured the PDRS, then the other rescue personnel can assist them immediately. In the part of hardware system, we used embedded system to be the primary part of the entire system, the embedded system has the characters of low power consumption and portability. Therefore, we chose the TI OMAP35x EVM platform to be our primary system of PDRS. In order to get the information of pedestrian, we also need the Inertial Measurement Unit (IMU) and Compass to provide the information of acceleration and heading for PDRS. To achieve the function of remote monitoring, we used wireless transmission module to send data of sensors to OMAP35x EVM. Finally, the most important function that we must accomplish in this thesis is to use OMAP35x EVM to build a real-time PDRS. In the part of software system, we use Linux OS and Qt SDK to build the software system of PDRS in this thesis. In the part of algorithm, we use step detection, step length estimation and dead reckoning method to construct the algorithm of PDRS in this thesis.

Nowadays mid-range smartphones have enough computational power to run simultaneous location and mapping (SLAM) algorithms that, together with their onboard inertial sensors makes them capable of position and rotation tracking. Based on this, Google and Apple have released their own respective software development kits (SDKs) that allow smartphones to run augmented reality applications using six degrees of freedom tracking. However, this same approach could be implemented to virtual reality head-mounted-display (HMD) based on smartphones, but current virtual reality SDKs only offer rotational tracking. In this study the positional tracking technology used for augmented reality mobile applications has been implemented in a virtual reality head-mounted-display only powered by a smartphone by combining virtual and augmented reality SDKs. Compatibility issues between SDKs have been faced to develop a working prototype. An objective and controlled measurement study has been conducted that included 34.200 measurements, to test the accuracy, precision and jitter tracking of the protype against the Oculus Rift, a dedicated virtual reality system. Results show that the developed prototype offers a decent tracking precision and accuracy in optimal conditions. It was concluded to be highly dependent on the camera view. Although, jitter presented the opposite behavior, being dependent to the device used but independent on the camera view. In its optimal conditions, user studies demonstrated that the prototype was capable of offering the same tracking performance feeling as the Oculus Rift although jitter was quite noticeable, and a common user complain. Further studies are proposed that can improve the tracking performance of the prototype by filtering jitter and using two or more cameras with a different angular to correlate feature points and obtain a wider view of the environment were the prototype is used. / Idag har mellanklass-smartphones tillräckligt med beräkningskapacitet för att simultant köra lokalisering och kartläggnings(SLAM) algoritmer tillsammans med deras tröghetssensorer ombord, vilket gör att de kan positionera och rotera spårning. Baserat på det här så har Google och Apple släppt sina egna respektive programvaror (SDK) som gör att smartphones kan köra ökade realitetsapplikationer med sex graders frihetsspårning. Emellertid kan samma tillvägagångssätt implementeras till virtuell verklighet på en huvudmonterad display (HMD) baserat på smartphones, men nuvarande VR SDK erbjuder endast rotationsspårning. I denna studie så har positionell spårningsteknik som används för AR i mobila applikationer implementerats i ett VRheadset som endast drivs av en smartphone genom att kombinera VR och ARSDKs. Kompatibilitetsproblem mellan SDKs har resulterat i att utveckla en fungerande prototyp. En objektiv och kontrollerad mätstudie har genomförts som inkluderade 34.200 mätningar, för att testa noggrannheten, precision och jitterspårning av protyp mot Oculus Rift, ett dedikerat virtuellt verklighetssystem. Resultat visar att den utvecklade prototypen ger en anständig spårningsprecision och noggrannhet i optimala betingelser. Denna slutsats var mycket beroende av kameravy. Även om jitter presenterade det motsatta beteendet, beroende på vilken enhet som används men oberoende av kamerans vy. I sina optimala förhållanden visade användarstudier att prototypen kunde erbjuda samma spårningsförmåga som Oculus Rift, även om jitter var ganska märkbar, och en vanlig användares klagomål. Ytterligare studier föreslås som kan förbättra prototypens spårningsprestanda genom att filtrera jitter och använder två eller flera kameror med en annan vinkling till att korrelera funktionspunkter och få en bredare bild av miljön var prototypen används.

L'objectif général de cette thèse était de proposer une solution de localisation en intérieur à la fois simple et capable de surmonter les défis de la propagation dans les environnements en intérieur. Pour ce faire, un système de localisation basé sur la méthode des signatures et adoptant le temps d'arrivée du signal de l'émetteur au récepteur comme signature, a été proposé. Le système présente deux architectures différentes, une première orientée privée utilisant la méthode d'accès multiple à répartition par code et une deuxième centralisée basée sur la méthode d'accès multiple à répartition dans le temps. Le système calcule la position de l'objet d'intérêt par la méthode de noyau. Une comparaison expérimentale entre le système à architecture orientée privée et un système de localisation sonore déjà existant et basé sur la méthode de trilatération, a permis de confirmer les résultats trouvés dans le cas de la localisation par ondes radiofréquences. Cependant, nos expérimentations étaient les premières à montrer l'effet de la réverbération sur les approches de la localisation acoustique. Dans un second lieu, un système de localisation basé sur la technique de retournement temporel, permettant une localisation simultanée de sources avec différentes précisions, a été testé par simulations en faisant varier le nombre de sources. Ce système a été ensuite validé par expérimentations. Dans la dernière partie de notre étude, nous nous sommes intéressés à la réduction de l'audibilité du signal utile à la localisation par recours à la psycho-acoustique. Un filtre défini à partir du seuil d'audition absolu a été appliqué au signal de localisation. Nos résultats ont montré une amélioration de la précision de localisation comparé au système de localisation sans modèle psycho-acoustique et ce grâce à l'utilisation d'un filtre adapté au modèle psycho-acoustique à la réception. Par ailleurs, l'écoute du signal après application du modèle psycho-acoustique a montré une réduction significative de son audibilité comparée à celle du signal original. / The objective of this PhD is to propose a location solution that should be simple and robust to multipath that characterizes the indoor environments. First, a location system that exploits the time domain of channel parameters has been proposed. The system adopts the time of arrival of the path of maximum amplitude as a signature and estimates the target position through nonparametric kernel regression. The system was evaluated in experiments for two main configurations: a privacy-oriented configuration with code-division multiple-access operation and a centralized configuration with time-division multiple-access operation. A comparison between our privacy-oriented system and another acoustic location system based on code-division multiple-access operation and lateration method confirms the results found in radiofrequency-based localization. However, our experiments are the first to demonstrate the detrimental effect that reverberation has on acoustic localization approaches. Second, a location system based on time reversal technique and able to localize simultaneously sources with different location precisions has been tested through simulations for different values of the number of sources. The system has then been validated by experiments. Finally, we have been interested in reducing the audibility of the localization signal through psycho-acoustics. A filter, set from the absolute threshold of hearing, is then applied to the signal. Our results showed an improvement in precision, when compared to the location system without psychoacoustic model, thanks to the use of matched filter at the receiver. Moreover, we have noticed a significant reduction in the audibility of the filtered signal compared to that of the original signal.