Stabilizing cell phone video using inertial measurement sensors. In:

Abstract We present a system that rectifies and stabilizes video sequences on mobile devices with rolling-shutter cameras. The system corrects for rolling-shutter distortions using measurements from accelerometer and gyroscope sensors, and a 3D rotational distortion model. In order to obtain a stabilized video, and at the same time keep most content in view, we propose an adaptive low-pass filter algorithm to obtain the output camera trajectory. The accuracy of the orientation estimates has been evaluated experimentally using ground truth data from a motion capture system. We have conducted a user study, where the output from our system, implemented in iOS, has been compared to that of three other applications, as well as to the uncorrected video. The study shows that users prefer our sensor-based system

Diet and body constitution in relation to subgroups of breast cancer defined by tumour grade, proliferation and key cell cycle regulators

BACKGROUND: The general lack of clear associations between diet and breast cancer in epidemiological studies may partly be explained by the fact that breast cancer is a heterogeneous disease that may have disparate genetic associations and different aetiological bases. METHOD: A total of 346 incident breast cancers in a prospective cohort of 17,035 women enrolled in the Malmö Diet and Cancer study (Sweden) were subcategorized according to conventional pathology parameters, proliferation and expression of key cell cycle regulators. Subcategories were compared with prediagnostic diet and body measurements using analysis of variance. RESULTS: A large hip circumference and high body mass index were associated with high grade tumours (P = 0.03 and 0.009, respectively), whereas low energy and unadjusted fat intakes were associated with high proliferation (P = 0.03 and 0.004, respectively). Low intakes of saturated, monounsaturated and polyunsaturated fatty acids were also associated with high proliferation (P = 0.02, 0.004 and 0.003, respectively). Low energy and unadjusted fat intakes were associated with cyclin D(1 )overexpression (P = 0.02 and 0.007, respectively), whereas cyclin E overexpression was positively correlated with fat intake. Oestrogen receptor status and expression of the tumour suppressor gene p27 were not associated with either diet or body constitution. CONCLUSION: Low energy and low total fat (polyunsaturated fatty acids in particular) intakes, and high body mass index were associated with relatively more malignant breast tumours. Dietary behaviours and body constitution may be associated with specific types of breast cancer defined by conventional pathology parameters and cyclin D(1 )and cyclin E expression. Further studies including healthy control individuals are needed to confirm our results

Topics in Localization and Mapping

The need to determine ones position is common and emerges in many different situations. Tracking soldiers or a robot moving in a building or aiding a tourist exploring a new city, all share the questions ”where is the unit?“ and ”where is the unit going?“. This is known as the localization problem.Particularly, the problem of determining ones position in a map while building the map at the same time, commonly known as the simultaneous localization and mapping problem (slam), has been widely studied. It has been performed in cities using different land bound vehicles, in rural environments using au- tonomous aerial vehicles and underwater for coral reef exploration. In this thesis it is studied how radar signals can be used to both position a naval surface ves- sel but also to simultaneously construct a map of the surrounding archipelago. The experimental data used was collected using a high speed naval patrol boat and covers roughly 32 km. A very accurate map was created using nothing but consecutive radar images.A second contribution covers an entirely different problem but it has a solution that is very similar to the first one. Underwater sensors sensitive to magnetic field disturbances can be used to track ships. In this thesis, the sensor positions them- selves are considered unknown and are estimated by tracking a friendly surface vessel with a known magnetic signature. Since each sensor can track the vessel, the sensor positions can be determined by relating them to the vessel trajectory. Simulations show that if the vessel is equipped with a global navigation satellite system, the sensor positions can be determined accurately.There is a desire to localize firefighters while they are searching through a burn- ing building. Knowing where they are would make their work more efficient and significantly safer. In this thesis a positioning system based on foot mounted in- ertial measurement units has been studied. When such a sensor is foot mounted, the available information increases dramatically since the foot stances can be de- tected and incorporated in the position estimate. The focus in this work has therefore been on the problem of stand still detection and a probabilistic frame- work for this has been developed. This system has been extensively investigated to determine its applicability during different movements and boot types. All in all, the stand still detection system works well but problems emerge when a very rigid boot is used or when the subject is crawling. The stand still detection frame- work was then included in a positioning framework that uses the detected stand stills to introduce zero velocity updates. The system was evaluated using local- ization experiments for which there was very accurate ground truth. It showed that the system provides good position estimates but that the estimated heading can be wrong, especially after quick sharp turns

Autonomous Localization in Unknown Environments

Over the last 20 years, navigation has almost become synonymous with satellite positioning, e.g. the Global Positioning System (GPS). On land, sea or in the air, on the road or in a city, knowing ones position is a question of getting a clear line of sight to enough satellites. Unfortunately, since the signals are extremely weak there are environments the GPS signals cannot reach but where positioning is still highly sought after, such as indoors and underwater. Also, because the signals are so weak, GPS is vulnerable to jamming. This thesis is about alternative means of positioning for three scenarios where gps cannot be used. Indoors, there is a desire to accurately position first responders, police officers and soldiers. This could make their work both safer and more efficient. In this thesis an inertial navigation system using a foot mounted inertial magnetic mea- surement unit is studied. For such systems, zero velocity updates can be used to significantly reduce the drift in distance travelled. Unfortunately, the estimated direction one is moving in is also subject to drift, causing large positioning errors. We have therefore chosen to throughly study the key problem of robustly estimating heading indoors. To measure heading, magnetic field measurements can be used as a compass. Unfortunately, they are often disturbed indoors making them unreliable. For estimation support, the turn rate of the sensor can be measured by a gyro but such sensors often have bias problems. In this work, we present two different approaches to estimate heading despite these shortcomings. Our first system uses a Kalman filter bank that recursively estimates if the magnetic readings are disturbed or undisturbed. Our second approach estimates the entire history of headings at once, by matching integrated gyro measurements to a vector of magnetic heading measurements. Large scale experiments are used to evaluate both methods. When the heading estimation is incorporated into our positioning system, experiments show that positioning errors are reduced significantly. We also present a probabilistic stand still detection framework based on accelerometer and gyro measurements. The second and third problems studied are both maritime. Naval navigation systems are today heavily dependent on GPS. Since GPS is easily jammed, the vessels are vulnerable in critical situations. In this work we describe a radar based backup positioning system to be used in case of GPS failure. radar scans are matched using visual features to detect how the surroundings have changed, thereby describing how the vessel has moved. Finally, we study the problem of underwater positioning, an environment gps signals cannot reach. A sensor network can track vessels using acoustics and the magnetic disturbances they induce. But in order to do so, the sensors themselves first have to be accurately positioned. We present a system that positions the sensors using a friendly vessel with a known magnetic signature and trajectory. Simulations show that by studying the magnetic disturbances that the vessel produces, the location of each sensor can be accurately estimated

Large Scale SLAM in an Urban Environment

Simultaneous Localisation And Mapping SLAM-problemet är ett robotikproblem som består av att låta en robot kartlägga ett tidigare okänt område, och samtidigt lokalisera sig i den skapade kartan. Det här exjobbet presenterar ett försök till en lösning på SLAM-problemet som fungerar i konstant tid i en urban miljö. En sådan lösning måste hantera en datamängd som ständigt ökar, utan att beräkningskomplexiteten ökar signifikant. Ett informationsfilter på fördröjd tillståndsform används för estimering av robotens trajektoria, och kamera och laseravståndssensorer används för att samla spatial information om omgivningarna längs färdvägen. Två olika metoder för att detektera loopslutningar föreslås. Den första är bildbaserad och använder Tree of Words för jämförelse av bilder. Den andra metoden är laserbaserad och använder en tränad klassificerare för att jämföra laserscans. När två posar, position och riktning, kopplats samman i en loopslutning beräknas den relativa posen med laserscansinriktning med hjälp av en kombination av Conditional Random Field-Match och Iterative Closest Point. Experiment visar att både bild- och laserscansbaserad loopslutningsdetektion fungerar bra i stadsmiljö, och resulterar i good estimering av kartan såväl som robotens trajektoria.In robotics, the Simultaneous Localisation And Mapping SLAM problem consists of letting a robot map a previously unknown environment, while simultaneously localising the robot in the same map. In this thesis, an attempt to solve the SLAM problem in constant time in a complex environment, such as a suburban area, is made. Such a solution must handle increasing amounts of data without significant increase in computation time. A delayed state information filter is used to estimate the robot's trajectory, and camera and laser range sensors are used to acquire spatial information about the environment along the trajectory. Two approaches to loop closure detection are proposed. The first is image based using Tree of Words for image comparison. The second is laser based using a trained classifier for laser scan comparison. The relative pose, the difference in position and heading, of two poses matched in loop closure is calculated with laser scan alignment using a combination of Conditional Random Field-Match and Iterative Closest Point. Experiments show that both image and laser based loop closure detection works well in a suburban area, and results in good estimation of the map as well as the robot's trajectory

Learning to Detect Loop Closure from Range Data

Despite signicant developments in the Simultaneous Localisation and Map- ping (slam) problem, loop closure detection is still challenging in large scale unstructured environments. Current solutions rely on heuristics that lack generalisation properties, in particular when range sensors are the only source of information about the robot's surrounding environment. This paper presents a machine learning approach for the loop closure detection problem using range sensors. A binary classier based on boosting is used to detect loop closures. The algorithm performs robustly, even under potential occlusions and signicant changes in rotation and translation. We devel- oped a number of features, extracted from range data, that are invariant to rotation. Additionally, we present a general framework for scan-matching slam in outdoor environments. Experimental results in large scale urban environments show the robustness of the approach, with a detection rate of 85% and a false alarm rate of only 1%. The proposed algorithm can be computed in real-time and achieves competitive performance with no manual specication of thresholds given the features

Robust Heading Estimation Indoors using Convex Optimization

The problem of estimating heading is central in the indoor positioning problem based on mea- surements from inertial measurement and magnetic units. Integrating rate of turn angular rate gives the heading with unknown initial condition and a linear drift over time, while the magnetometer gives absolute heading, but where long segments of data are useless in prac- tice because of magnetic disturbances. A basic Kalman filter approach with outlier rejection has turned out to be difficult to use with high integrity. Here, we propose an approach based on convex optimization, where segments of good magnetometer data are separated from disturbed data and jointly fused with the yaw rate measurements. The optimization framework is flexible with many degrees of freedom in the modeling phase, and we outline one design. A recursive solution to the optimization is derived, which has a computational complexity comparable to the simplest possible Kalman filter. The performance is evaluated using data from a handheld smartphone for a large amount of indoor trajectories, and the result demonstrates that the method effectively resolves the magnetic disturbances