This thesis investigates techniques and designs an autonomous visual stereo based navigation sensor to improve stereo visual odometry for purpose of navigation in unknown environments. In particular, autonomous navigation in a space mission context which imposes challenging constraints on algorithm development and hardware requirements. For instance, Global Positioning System (GPS) is not available in this context. Thus, a solution for navigation cannot rely on similar external sources of information. Support to handle this problem is required with the conception of an intelligent perception-sensing device that provides precise outputs related to absolute and relative 6 degrees of freedom (DOF) positioning. This is achieved using only images from stereo calibrated cameras possibly coupled with an inertial measurement unit (IMU) while fulfilling real time processing requirements. Moreover, no prior knowledge about the environment is assumed. Robotic navigation has been the motivating research to investigate different and complementary areas such as stereovision, visual motion estimation, optimisation and data fusion. Several contributions have been made in these areas. Firstly, an efficient feature detection, stereo matching and feature tracking strategy based on Kanade-Lucas-Tomasi (KLT) feature tracker is proposed to form the base of the visual motion estimation. Secondly, in order to cope with extreme illumination changes, High dynamic range (HDR) imaging solution is investigated and a comparative assessment of feature tracking performance is conducted. Thirdly, a two views local bundle adjustment scheme based on trust region minimisation is proposed for precise visual motion estimation. Fourthly, a novel KLT feature tracker using IMU information is integrated into the visual odometry pipeline. Finally, a smart standalone stereo visual/IMU navigation sensor has been designed integrating an innovative combination of hardware as well as the novel software solutions proposed above. As a result of a balanced combination of hardware and software implementation, we achieved 5fps frame rate processing up to 750 initials features at a resolution of 1280x960. This is the highest reached resolution in real time for visual odometry applications to our knowledge. In addition visual odometry accuracy of our algorithm achieves the state of the art with less than 1% relative error in the estimated trajectories.

This thesis investigates techniques and designs an autonomous visual stereo based navigation sensor to improve stereo visual odometry for purpose of navigation in unknown environments. In particular, autonomous navigation in a space mission context which imposes challenging constraints on algorithm development and hardware requirements. For instance, Global Positioning System (GPS) is not available in this context. Thus, a solution for navigation cannot rely on similar external sources of information. Support to handle this problem is required with the conception of an intelligent perception-sensing device that provides precise outputs related to absolute and relative 6 degrees of freedom (DOF) positioning. This is achieved using only images from stereo calibrated cameras possibly coupled with an inertial measurement unit (IMU) while fulfilling real time processing requirements. Moreover, no prior knowledge about the environment is assumed. Robotic navigation has been the motivating research to investigate different and complementary areas such as stereovision, visual motion estimation, optimisation and data fusion. Several contributions have been made in these areas. Firstly, an efficient feature detection, stereo matching and feature tracking strategy based on Kanade-Lucas-Tomasi (KLT) feature tracker is proposed to form the base of the visual motion estimation. Secondly, in order to cope with extreme illumination changes, High dynamic range (HDR) imaging solution is investigated and a comparative assessment of feature tracking performance is conducted. Thirdly, a two views local bundle adjustment scheme based on trust region minimisation is proposed for precise visual motion estimation. Fourthly, a novel KLT feature tracker using IMU information is integrated into the visual odometry pipeline. Finally, a smart standalone stereo visual/IMU navigation sensor has been designed integrating an innovative combination of hardware as well as the novel software solutions proposed above. As a result of a balanced combination of hardware and software implementation, we achieved 5fps frame rate processing up to 750 initials features at a resolution of 1280x960. This is the highest reached resolution in real time for visual odometry applications to our knowledge. In addition visual odometry accuracy of our algorithm achieves the state of the art with less than 1% relative error in the estimated trajectories. / © Cranfield University, 2014

GPS/IMU integrated systems are commonly used for vehicle navigation. The algorithm for this coupled system is normally based on Kalman filter. However, oscillated GPS measurements in the urban environment can lead to localization divergence easily. Moreover, heading estimation may be sensitive to magnetic interference if it relies on IMU with integrated magnetometer. This report tries to solve the localization problem on GPS oscillation and outage, based on adaptive extended Kalman filter(AEKF). In terms of the heading estimation, stereo visual odometry(VO) is fused to overcome the effect by magnetic disturbance. Vision-aided AEKF based algorithm is tested in the cases of both good GPS condition and GPS oscillation with magnetic interference. Under the situations considered, the algorithm is verified to outperform conventional extended Kalman filter(CEKF) and unscented Kalman filter(UKF) in position estimation by 53.74% and 40.09% respectively, and decrease the drifting of heading estimation. / GPS/IMU integrerade system används ofta för navigering av fordon. Algoritmen för detta kopplade system är normalt baserat på ett Kalmanfilter. Ett problem med systemet är att oscillerade GPS mätningar i stadsmiljöer enkelt kan leda till en lokaliseringsdivergens. Dessutom kan riktningsuppskattningen vara känslig för magnetiska störningar om den är beroende av en IMU med integrerad magnetometer. Rapporten försöker lösa lokaliseringsproblemet som skapas av GPS-oscillationer och avbrott med hjälp av ett adaptivt förlängt Kalmanfilter (AEKF). När det gäller riktningsuppskattningen används stereovisuell odometri (VO) för att försvaga effekten av magnetiska störningar genom sensorfusion. En Visionsstödd AEKF-baserad algoritm testas i fall med både goda GPS omständigheter och med oscillationer i GPS mätningar med magnetiska störningar. Under de fallen som är aktuella är algoritmen verifierad för att överträffa det konventionella utökade Kalmanfilteret (CEKF) och ”Unscented Kalman filter” (UKF) när det kommer till positionsuppskattning med 53,74% respektive 40,09% samt minska fel i riktningsuppskattningen.