Recent studies have demonstrated that images constructed from lidar reflectance information exhibit superior robustness to lighting changes. However, due to the scanning nature of the lidar and assumptions made in previous implementations, data acquired during continuous vehicle motion suffer from geometric motion distortion and can subsequently result in poor metric visual odometry (VO) estimates, even over short distances (e.g., 5-10 m). The first part of this thesis revisits the measurement timing assumption made in previous systems, and proposes a frame-to-frame VO estimation framework based on a pose-interpolation scheme that explicitly accounts for the exact acquisition time of each intrinsic, geometric feature measurement. The second part of this thesis investigates a novel method of lidar calibration that can be applied without consideration of the internal structure of the sensor. Both methods are validated using experimental data collected from a planetary analogue environment with a real scanning laser rangefinder.

Recent studies have demonstrated that images constructed from lidar reflectance information exhibit superior robustness to lighting changes. However, due to the scanning nature of the lidar and assumptions made in previous implementations, data acquired during continuous vehicle motion suffer from geometric motion distortion and can subsequently result in poor metric visual odometry (VO) estimates, even over short distances (e.g., 5-10 m). The first part of this thesis revisits the measurement timing assumption made in previous systems, and proposes a frame-to-frame VO estimation framework based on a pose-interpolation scheme that explicitly accounts for the exact acquisition time of each intrinsic, geometric feature measurement. The second part of this thesis investigates a novel method of lidar calibration that can be applied without consideration of the internal structure of the sensor. Both methods are validated using experimental data collected from a planetary analogue environment with a real scanning laser rangefinder.

A classic Computer Vision task is the estimation of a 3D map from a collection of images. This thesis explores the online simultaneous estimation of camera poses and map points, often called Visual Simultaneous Localisation and Mapping [VSLAM]. In the near future the use of visual information by autonomous cars is likely, since driving is a vision dominated process. For example, VSLAM could be used to estimate the position of the car in relation to objects of interest, such as the road, other cars and pedestrians. Aimed at the creation of a real-time, robust, loop closing, single camera SLAM system, the properties of several state-of-the-art VSLAM systems and related techniques are studied. The system goals cover several important, if difficult, problems, which makes a solution widely applicable. This thesis makes two contributions: A rigorous qualitative analysis of VSLAM methods and a system designed accordingly. A novel tracking by matching scheme is proposed, which, unlike the trackers used by many similar systems, is able to deal better with forward camera motion. The system estimates general motion with loop closure in real time. The system is compared to a state-of-the-art monocular VSLAM algorithm and found to be similar in speed and performance.

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.

In this research, image segmentation and visual odometry estimations in real time are addressed, and two main contributions were made to this field. First, a new image segmentation and classification algorithm named DilatedU-NET is introduced. This deep learning based algorithm is able to process seven frames per-second and achieves over 84% accuracy using the Cityscapes dataset. Secondly, a new method to estimate visual odometry is introduced. Using the KITTI benchmark dataset as a baseline, the visual odometry error was more significant than could be accurately measured. However, the robust framerate speed made up for this, able to process 15 frames per second. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

Accurate registration of 3D data is one of the most challenging problems in a number of Computer Vision applications. Visual Odometry is one such application, which determines the motion, or change in position of a moving rover by registering 3D data captured by an on-board range sensor, in a pairwise manner. The performance of Visual Odometry depends upon two main factors, the first being the quality of 3D data, which itself depends upon the type of sensor being used. The second factor is the robustness of the registration algorithm. Where sensors like stereo cameras and LIDAR scanners have been used in the past to improve the performance of Visual Odometry, the introduction of the Velodyne LIDAR scanner is fairly new and has been less investigated, particularly for odometry applications. This thesis presents and examines a new method for registering 3D point clouds generated by a Velodyne scanner mounted on a moving rover. The method is based on one of the the most widely used registration algorithms called Iterative Closest Point (ICP). The proposed method is divided into two steps. The first step, which is also the main contribution of this work, is the introduction of a new point sampling method, which prudently select points that belong to the regions of greatest geometric variance in the scan. Interest Point (Region) Sampling plays an important role in the performance of ICP by effectively discounting the regions with non-uniform resolution and selecting regions with a high geometric variance and uniform resolution. Second step is to use sampled scan pairs as the input to a new plane-to-plane variant of ICP, known as Generalized ICP. Several experiments have been executed to test the compatibility and robustness of Interest Point Sampling (IPS) for a variety of terrain landscapes. Through these experiments, which include comparisons of variants of ICP and past sampling methods, this work demonstrates that the combination of IPS and GICP results in the least localization error as compared to all other tested method. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2011-11-03 11:12:43.596

Due to the absence of any satellite-based global positioning system on Mars, the Mars Exploration Rovers commonly track position changes of the vehicle using a technique called visual odometry (VO), where updated rover poses are determined by tracking keypoints between stereo image pairs. Unfortunately, the error of VO grows super-linearly with the distance traveled, primarily due to the contribution of orientation error. This thesis outlines a novel approach incorporating sun sensor and inclinometer measurements directly into the VO pipeline, utilizing absolute orientation information to reduce the error growth of the motion estimate. These additional measurements have very low computation, power, and mass requirements, providing a localization improvement at nearly negligible cost. The mathematical formulation of this approach is described in detail, and extensive results are presented from experimental trials utilizing data collected during a 10 kilometre traversal of a Mars analogue site on Devon Island in the Canadian High Arctic.

Due to the absence of any satellite-based global positioning system on Mars, the Mars Exploration Rovers commonly track position changes of the vehicle using a technique called visual odometry (VO), where updated rover poses are determined by tracking keypoints between stereo image pairs. Unfortunately, the error of VO grows super-linearly with the distance traveled, primarily due to the contribution of orientation error. This thesis outlines a novel approach incorporating sun sensor and inclinometer measurements directly into the VO pipeline, utilizing absolute orientation information to reduce the error growth of the motion estimate. These additional measurements have very low computation, power, and mass requirements, providing a localization improvement at nearly negligible cost. The mathematical formulation of this approach is described in detail, and extensive results are presented from experimental trials utilizing data collected during a 10 kilometre traversal of a Mars analogue site on Devon Island in the Canadian High Arctic.

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

In pursuit of creating a fully automated navigation system that is capable of operating in dynamic environments, a large amount of research is being devoted to systems that use visual odometry assisted methods to estimate the position of a platform with regards to the environment surrounding it. This includes systems that do and do not know the environment a priori, as both rely on the same methods for localisation. For the combined problem of localisation and mapping, Simultaneous Localisation and Mapping (SLAM) is the de facto choice, and in recent years with the advent of color and depth (RGB-D) sensors, RGB-D SLAM has become a hot topic for research. Most research being performed is on improving the overall system accuracy or more specifically the performance with regards to the overall trajectory error. While this approach quantifies the performance of the system as a whole, the individual frame-to-frame performance is often not mentioned or explored properly. While this will directly tie in to the overall performance, the level of scene cohesion experienced between two successive observations can vary greatly over a single dataset of observations. The focus of this dissertation will be the relevant levels of translational and rotational velocities experienced by the sensor between two successive observations and the effect on the final accuracy of the SLAM implementation. The frame rate will specifically be used to alter and evaluate the different spatial velocities experienced over multiple datasets of RGB-D data. Two systems were developed to illustrate and evaluate the potential of various approaches to RGB-D SLAM. The first system is a real-world implementation where SLAM is used to localise and map the environment surrounding a quadcopter platform. A Microsoft Kinect is directly mounted to the quadcopter and is used to provide a RGB-D datastream to a remote processing terminal. This terminal runs a SLAM implementation that can alternate between different visual odometry methods. The remote terminal acts as the position controller for the quadcopter, replacing the need for a direct human operator. A semi-automated system is implemented, that allows a human operator to designate waypoints within the environment that the quadcopter moves to. The second system uses a series of publicly available RGB-D datasets with their accompanying ground-truth readings to simulate a real RGB-D datasteam. This is used to evaluate the performance of the various RGB-D SLAM approaches to visual odometry. For each of the datasets, the accompanying translational and angular velocity on a frame-to-frame basis can be calculated. This can, in turn, be used to evaluate the frame-to-frame accuracy of the SLAM implementation, where the spatial velocity can be manually altered by occluding frames within the sequence. Thus, an accurate relationship can be calculated between the frame rate, the spatial velocity and the performance of the SLAM implementation. Three image processing techniques were used to implement the visual odometry for RGB-D SLAM. SIFT, SURF and ORB were compared across eight of the TUM database datasets. SIFT had the best performance, with a 30% increase over SURF and doubling the performance of ORB. By implementing SIFT using CUDA, the feature detection and description process only takes 18ms, negating the disadvantage that SIFT has compared to SURF and ORB. The RGB-D SLAM implementation was compared to four prominent research papers, and showed comparable results. The effect of rotation and translation was evaluated, based on the effect of each rotation and translation axis. It was found that the z-axis (scale) and the roll-axis (scene orientation) have a lower effect on the average RPE error in a frame-to-frame basis. It was found that rotation has a much greater impact on the performance, when evaluating rotation and translation separately. On average, a rotation of 1deg resulted in a 4mm translation error and a 20% rotation error , where a translation of 10mm resulted in a rotation error of 0.2deg and a translation error of 45%. The combined effect of rotation and translation had a multiplicative effect on the error metric. The quadcopter platform designed to work with the SLAM implementation did not function ideally, but it was sufficient for the purpose. The quadcopter is able to self stabilise within the environment, given a spacious area. For smaller, enclosed areas the backdraft generated by the quadcopter motors lead to some instability in the system. A frame-to-frame error of 40.34mm and 1.93deg was estimated for the quadcopter system. / Dissertation (MEng)--University of Pretoria, 2017. / Electrical, Electronic and Computer Engineering / MEng / Unrestricted