<p>This report contains a description on how it is possible to guide a robot along an edge, by using a camera mounted on the robot. If stereo matching is used to calculate 3Dcoordinates of an object or an edge, it requires two images from different known positions and orientations to calculate where it is. In the image analysis in this project, the Canny edge filter has been used. The result from the filter is not useful directly, because it finds too many edges and it misses some pixels. The Canny edge result must be sorted and finally filled up before the final calculations can be started. This additional work with the image decreases unfortunately the accuracy in the calculations. The accuracy is estimated through comparison between measured coordinates of the edge using a coordinate measuring machine and the calculated coordinates. There is a deviation of up to three mm in the calculated edge. The camera calibration has been described in earlier thesis so it is not mentioned in this report, although it is a prerequisite of this project.</p>

The problem of constructing a surface from the information provided by the Marr-Poggio theory of human stereo vision is investigated. It is argued that not only does this theory provide explicit boundary conditions at certain points in the image, but that the imaging process also provides implicit conditions on all other points in the image. This argument is used to derive conditions on possible algorithms for computing the surface. Additional constraining principles are applied to the problem; specifically that the process be performable by a local-support parallel network. Some mathematical tools, differential geometry, Coons surface patches and iterative methods of convergence, relevant to the problem of constructing the surface are outlined. Specific methods for actually computing the surface are examined.

This thesis addresses the issue of ground collision in poor weather conditions. As bad weather is an adverse factor when airplanes are taxiing, an obstacle detection system based on thermal vision is proposed to enhance the awareness of pilots during taxiing in poor weather conditions. Two infrared cameras are employed to detect the objects and estimate the distance of the obstacle. The distance is computed by stereo vision technology. A warning will be given if the distance is less than the safe distance predefined. To make the system independent, the proposed system is an on-board system which does not rely on airports or other airplanes. The type of obstacle is classified by the temperature of the object. Fuzzy logic is employed in the classification. Obstacles are classified into three main categories: aircraft, vehicle and people. Membership functions are built based on the temperature distribution of obstacles measured at the airport. In order to improve the accuracy of classification, a concept of using position information is proposed. Different types of obstacle are predefined according to different area at the airport. In the classification, obstacles are classified according to the types limited in that area. Due to the limitation of the thermal infrared camera borrowed, images were captured first and then processed offline. Experiments were carried out to evaluate the detecting distance error and the performance of system in poor weather conditions. The classification of obstacle is simulated with real thermal images and pseudo position information at the airport. The results suggest that the stereo vision system developed in this research was able to detect the obstacle and estimate the distance. The classification method classified the obstacles to a certain extent. Therefore, the proposed system can improve safety of aircraft and enhance situational awareness of pilots. The programming language of the system is Python 2.7. Computer graphic library OpenCV 2.3 is used in processing images. MATLAB is used in the simulation of obstacle classification.

This report contains a description on how it is possible to guide a robot along an edge, by using a camera mounted on the robot. If stereo matching is used to calculate 3Dcoordinates of an object or an edge, it requires two images from different known positions and orientations to calculate where it is. In the image analysis in this project, the Canny edge filter has been used. The result from the filter is not useful directly, because it finds too many edges and it misses some pixels. The Canny edge result must be sorted and finally filled up before the final calculations can be started. This additional work with the image decreases unfortunately the accuracy in the calculations. The accuracy is estimated through comparison between measured coordinates of the edge using a coordinate measuring machine and the calculated coordinates. There is a deviation of up to three mm in the calculated edge. The camera calibration has been described in earlier thesis so it is not mentioned in this report, although it is a prerequisite of this project.

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The prevalence of obesity has necessitated developing safe and convenient tools for timely assessing and monitoring this condition for a broad range of population. Three-dimensional (3D) body imaging has become a new mean for obesity assessment. Moreover, it generates body shape information that is meaningful for fitness, ergonomics, and personalized clothing. In the previous work of our lab, we developed a prototype active stereo vision system that demonstrated a potential to fulfill this goal. But the prototype required four computer projectors to cast artificial textures on the body which facilitate the stereo-matching on texture-deficient images (e.g., skin). This decreases the mobility of the system when used to collect a large population data. In addition, the resolution of the generated 3D~images is limited by both cameras and projectors available during the project. The study reported in this dissertation highlights our continued effort in improving the capability of 3Dbody imaging through simplified hardware for passive stereo and advanced computation techniques. The system utilizes high-resolution single-lens reflex (SLR) cameras, which became widely available lately, and is configured in a two-stance design to image the front and back surfaces of a person. A total of eight cameras are used to form four pairs of stereo units. Each unit covers a quarter of the body surface. The stereo units are individually calibrated with a specific pattern to determine cameras' intrinsic and extrinsic parameters for stereo matching. The global orientation and position of each stereo unit within a common world coordinate system is calculated through a 3Dregistration step. The stereo calibration and 3Dregistration procedures do not need to be repeated for a deployed system if the cameras' relative positions have not changed. This property contributes to the portability of the system, and tremendously alleviates the maintenance task. The image acquisition time is around two seconds for a whole-body capture. The system works in an indoor environment with a moderate ambient light. Advanced stereo computation algorithms are developed by taking advantage of high-resolution images and by tackling the ambiguity problem in stereo matching. A multi-scale, coarse-to-fine matching framework is proposed to match large-scale textures at a low resolution and refine the matched results over higher resolutions. This matching strategy reduces the complexity of the computation and avoids ambiguous matching at the native resolution. The pixel-to-pixel stereo matching algorithm follows a classic, four-step strategy which consists of matching cost computation, cost aggregation, disparity computation and disparity refinement. The system performance has been evaluated on mannequins and human subjects in comparison with other measurement methods. It was found that the geometrical measurements from reconstructed 3Dbody models, including body circumferences and whole volume, are highly repeatable and consistent with manual and other instrumental measurements (CV < 0.1$%, R2>0.99). The agreement of percent body fat (%BF) estimation on human subjects between stereo and dual-energy X-ray absorptiometry (DEXA) was found to be improved over the previous active stereo system, and the limits of agreement with 95% confidence were reduced by half. Our achieved %BF estimation agreement is among the lowest ones of other comparative studies with commercialized air displacement plethysmography (ADP) and DEXA. In practice, %BF estimation through a two-component model is sensitive to body volume measurement, and the estimation of lung volume could be a source of variation. Protocols for this type of measurement should still be created with an awareness of this factor. / text

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Autonomous Driving or self driving vehicles are concepts of vehicles knowing their environment and making driving manoeuvres without instructions from a driver. The concepts have been around for decades but has improved significantly in the last years since research in this area has made significant progress. Benefits of autonomous driving include the possibility to decrease the number of accidents in traffic and thereby saving lives. A major challenge in autonomous driving is to acquire 3D information and relations between all objects in surrounding traffic. This is referred to as \textit{spatial perception}. Stereo camera systems have become a central sensor module for advanced driver assistance systems and autonomous driving. For object detection and measurements at large distances stereo vision encounter difficulties. This includes objects being small, having low contrast and the presence of image noise. Having an accurate perception of the environment at large distances is however of high interest for many applications, especially autonomous driving. This thesis proposes a method which tries to increase the range to where generic objects are first detected using a given stereo camera setup. Objects are represented by planes in 3D space. The input image is segmented into the various objects and the 3D plane parameters are estimated jointly. The 3D plane parameters are estimated directly from the stereo image pairs. In particular, this thesis investigates methods to introduce geometric constraints to the segmentation or labeling task, i.e assigning each considered pixel in the image to a plane. The methods provided in this thesis show that despite the difficulties at large distances it is possible to exploit planar primitives in 3D space for obstacle detection at distances where other methods fail. / En autonom bil innebär att bilen har en uppfattning om sin omgivning och kan utifran det ta beslut angående hur bilen ska manövreras. Konceptet med självkörande bilar har existerat i årtionden men har utvecklats snabbt senaste åren sedan billigare datorkraft finns lättare tillgänglig. Fördelar med autonomiska bilar innebär bland annat att antalet olyckor i trafiken minskas och därmed liv räddas. En av de största utmaningarna med autonoma bilar är att få 3D information och relationer mellan objekt som finns i den omgivande trafikmiljön. Detta kallas för spatial perception och innebär att detektera alla objekt och tilldela en korrekt postition till dem. Stereo kamerasystem har fått en central roll för avancerade förarsystem och autonoma bilar. För detektion av objekt på stora avstånd träffar stereo system på svårigheter. Detta inkluderar väldigt små objekt, låg kontrast och närvaron av brus i bilden. Att ha en ackurativ perception på stora avstånd är dock vitalt för många applikationer, inte minst autonoma bilar. Den här rapporten föreslar en metod som försöker öka avståndet till där objekt först upptäcks. Objekt representeras av plan i 3D rymden. Bilder givna från stereo par segmenteras i olika object och plan parametrar estimeras samtidigt. Planens parametrar estimeras direkt från stereo bild paren. Den här rapporten utreder metoder att introducera gemoetriska begränsningar att använda vid segmenteringsuppgiften. Metoderna som presenteras i denna rapport visar att trots den höga närvaron av brus på stora avstånd är det möjligt att estimera geometriska objekt som är starka nog att möjliggöra detektion av objekt på ett avstand där andra metoder misslyckas.

Stereopsis is the process of inferring the distance to objects from two or more images. It has applications in areas such as: novel-view rendering, motion capture, autonomous navigation, and topographical mapping from remote sensing data. Although it sounds simple, in light of the effortlessness with which we are able to perform the task with our own eyes, a number of factors that make it quite challenging become apparent once one begins delving into computational methods of solving it. For example, occlusions that block part of the scene from being seen in one of the images, and changes in the appearance of objects between the two images due to: sensor noise, view dependent effects, and/or differences in the lighting/camera conditions between the two images. Global stereopsis algorithms aim to solve this problem by making assumptions about the smoothness of the depth of surfaces in the scene, and formulating stereopsis as an optimization problem. As part of their formulation, these algorithms include a function that measures the similarity between pixels in different images to detect possible correspondences. Which of these match cost functions work better, when, and why is not well understood. Furthermore, in areas of computer vision such as segmentation, face detection, edge detection, texture analysis and classification, and optical flow, it is not uncommon to use colour spaces other than the well known RGB space to improve the accuracy of algorithms. However, the use of colour spaces other than RGB is quite rare in stereopsis research. In this dissertation we present results from two, first of their kind, large scale studies on global stereopsis algorithms. In the first we compare the relative performance of a structured set of match cost cost functions in five different global stereopsis frameworks in such a way that we are able to infer some general rules to guide the choice of which match cost functions to use in these algorithms. In the second we investigate how much accuracy can be gained by simply changing the colour representation used in the input to global stereopsis algorithms.

Thesis (MEng)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: This thesis investigates the use of stereo vision sensors for dense autonomous mapping. It characterises and analyses the errors made during the stereo matching process so measurements can be correctly integrated into a 3D grid-based map. Maps are required for navigation and obstacle avoidance on autonomous vehicles in complex, unknown environments. The safety of the vehicle as well as the public depends on an accurate mapping of the environment of the vehicle, which can be problematic when inaccurate sensors such as stereo vision are used. Stereo vision sensors are relatively cheap and convenient, however, and a system that can create reliable maps using them would be beneficial. A literature review suggests that occupancy grid mapping poses an appropriate solution, offering dense maps that can be extended with additional measurements incrementally. It forms a grid representation of the environment by dividing it into cells, and assigns a probability to each cell of being occupied. These probabilities are updated with measurements using a sensor model that relates measurements to occupancy probabilities. Numerous forms of these sensor models exist, but none of them appear to be based on meaningful assumptions and sound statistical principles. Furthermore, they all seem to be limited by an assumption of unimodal, zero-mean Gaussian measurement noise. Therefore, we derive a principled inverse sensor model (PRISM) based on physically meaningful assumptions. This model is capable of approximating any realistic measurement error distribution using a Gaussian mixture model (GMM). Training a GMM requires a characterisation of the measurement errors, which are related to the environment as well as which stereo matching technique is used. Therefore, a method for fitting a GMM to the error distribution of a sensor using measurements and ground truth is presented. Since we may consider the derived principled inverse sensor model to be theoretically correct under its assumptions, we use it to evaluate the approximations made by other models from the literature that are designed for execution speed. We show that at close range these models generally offer good approximations that worsen with an increase in measurement distance. We test our model by creating maps using synthetic and real world data. Comparing its results to those of sensor models from the literature suggests that our model calculates occupancy probabilities reliably. Since our model captures the limited measurement range of stereo vision, we conclude that more accurate sensors are required for mapping at greater distances. / AFRIKAANSE OPSOMMING: Hierdie tesis ondersoek die gebruik van stereovisie sensors vir digte outonome kartering. Dit karakteriseer en ontleed die foute wat gemaak word tydens die stereopassingsproses sodat metings korrek geïntegreer kan word in 'n 3D rooster-gebaseerde kaart. Sulke kaarte is nodig vir die navigasie en hindernisvermyding van outonome voertuie in komplekse en onbekende omgewings. Die veiligheid van die voertuig sowel as die publiek hang af van 'n akkurate kartering van die voertuig se omgewing, wat problematies kan wees wanneer onakkurate sensors soos stereovisie gebruik word. Hierdie sensors is egter relatief goedkoop en gerieflik, en daarom behoort 'n stelsel wat hulle dit gebruik om op 'n betroubare manier kaarte te skep baie voordelig te wees. 'n Literatuuroorsig dui daarop dat die besettingsroosteralgoritme 'n geskikte oplossing bied, aangesien dit digte kaarte skep wat met bykomende metings uitgebrei kan word. Hierdie algoritme skep 'n roostervoorstelling van die omgewing en ken 'n waarskynlikheid dat dit beset is aan elke sel in die voorstelling toe. Hierdie waarskynlikhede word deur nuwe metings opgedateer deur gebruik te maak van 'n sensormodel wat beskryf hoe metings verband hou met besettingswaarskynlikhede. Menigde a eidings bestaan vir hierdie sensormodelle, maar dit blyk dat geen van die modelle gebaseer is op betekenisvolle aannames en statistiese beginsels nie. Verder lyk dit asof elkeen beperk word deur 'n aanname van enkelmodale, nul-gemiddelde Gaussiese metingsgeraas. Ons lei 'n beginselfundeerde omgekeerde sensormodel af wat gebaseer is op fisies betekenisvolle aannames. Hierdie model is in staat om enige realistiese foutverspreiding te weerspieël deur die gebruik van 'n Gaussiese mengselmodel (GMM). Dit vereis 'n karakterisering van 'n stereovisie sensor se metingsfoute, wat afhang van die omgewing sowel as watter stereopassingstegniek gebruik is. Daarom stel ons 'n metode voor wat die foutverspreiding van die sensor met behulp van 'n GMM modelleer deur gebruik te maak van metings en absolute verwysings. Die afgeleide ge inverteerde sensormodel is teoreties korrek en kan gevolglik gebruik word om modelle uit die literatuur wat vir uitvoerspoed ontwerp is te evalueer. Ons wys dat op kort afstande die modelle oor die algemeen goeie benaderings bied wat versleg soos die metingsafstand toeneem. Ons toets ons nuwe model deur kaarte te skep met gesimuleerde data, sintetiese data, en werklike data. Vergelykings tussen hierdie resultate en dié van sensormodelle uit die literatuur dui daarop dat ons model besettingswaarskynlikhede betroubaar bereken. Aangesien ons model die beperkte metingsafstand van stereovisie vasvang, lei ons af dat meer akkurate sensors benodig word vir kartering oor groter afstande.