Localization Using Visual Odometry and a Single Downward-Pointing Camera

Stereo imaging is a technique commonly employed for vision-based navigation. For such applications, two images are acquired from different vantage points and then compared using transformations to extract depth information. The technique is commonly used in robotics for obstacle avoidance or for Simultaneous Localization And Mapping, (SLAM). Yet, the process requires a number of image processing steps and therefore tends to be CPU-intensive, which limits the real-time data rate and use in power-limited applications. Evaluated here is a technique where a monocular camera is used for vision-based odometry. In this work, an optical flow technique with feature recognition is performed to generate odometry measurements. The visual odometry sensor measurements are intended to be used as control inputs or measurements in a sensor fusion algorithm using low-cost MEMS based inertial sensors to provide improved localization information. Presented here are visual odometry results which demonstrate the challenges associated with using ground-pointing cameras for visual odometry. The focus is for rover-based robotic applications for localization within GPS-denied environments

High-speed multi-dimensional relative navigation for uncooperative space objects

This work proposes a high-speed Light Detection and Ranging (LIDAR) based navigation architecture that is appropriate for uncooperative relative space navigation applications. In contrast to current solutions that exploit 3D LIDAR data, our architecture transforms the odometry problem from the 3D space into multiple 2.5D ones and completes the odometry problem by utilizing a recursive filtering scheme. Trials evaluate several current state-of-the-art 2D keypoint detection and local feature description methods as well as recursive filtering techniques on a number of simulated but credible scenarios that involve a satellite model developed by Thales Alenia Space (France). Most appealing performance is attained by the 2D keypoint detector Good Features to Track (GFFT) combined with the feature descriptor KAZE, that are further combined with either the H∞ or the Kalman recursive filter. Experimental results demonstrate that compared to current algorithms, the GFTT/KAZE combination is highly appealing affording one order of magnitude more accurate odometry and a very low processing burden, which depending on the competitor method, may exceed one order of magnitude faster computation

Електропривод рейкового транспорту з інтелектуальною системою керування

Purpose. The study of the conditions for the occurrence of frictional self-oscillations, the synthesis of a neuroregulator eliminating self-oscillation, the development of a system for automatic control of the of railway transport speed depending on the curvature of the track on the basis of computer vision technology. Methodology. Mathematical analysis and modeling. Findings. The paper presents the results of the development and research of an intelligent control system for the electric drive of a DS3 mainline electric locomotive. The developed systems have a single easily implemented motor speed feedback, which does not create difficulties in physical implementation. It is noted that a common feature of the electric drive of rail transport is a nonlinear load characteristic. It is shown that, under certain combinations of parameters, frictional self-oscillations are possible in the traction electric drive. Effective elimination of frictional self-oscillations is done  by synthesizing the system with a neuroregulator. The neural network has three input neurons that receive a vector of input signals in the form of a voltage signal, a signal of the motor speed value of the current and previous energy speed values. The number of neurons in the hidden layer of the system is 20 and there is one output neuron.The control actions for the frequency converter are formed on the output neuron. Neural networks of this type are designated NN3-20-1. The genetic algorithm method is used for all optimization of neural network parameters. The simulation model of the electric drive of rail transport has the integration of a computer vision unit. Increasing the level of automation and safety of rail vehicles is possible on the basis of computer vision. A feature of this structure is the presence of an NN neural regulator in it. NN ensures the specified quality of the transient process over the entire load range and when the operating point is located on a falling section. A system for automatic control of the speed of rail vehicles depending on the curvature of the track has been developed to increase the level of automation and traffic safety. Modeling of the system showed its efficiency, which is manifested in a decrease in the speed of rail vehicles when moving along a section of track with curvature. Originality.  Effective elimination of frictional self-oscillations due to the use of a neuroregulator. Practical value.  A system for automatically adjusting the speed of rail transport depending on the curvature of the track has been developed to increase the level of automation and traffic safety.Мета роботи. Дослідження умов виникнення фрикційних автоколивань, синтез нейрорегулятора усуваючого автоколивання, розробка системи автоматичного керування швидкістю руху залізничного транспорту в залежності від кривизни шляху на основі технології комп’ютерного зору. Методи дослідження. Математичний аналіз та моделювання. Отримані результати. У статті наведено результати розробки та досліджень інтелектуальної системи керування електроприводом магістрального електровоза ДС3. Розроблені системи мають єдиний легко реалізований зворотний зв’язок по швидкості двигуна, що не створює труднощів у фізичній реалізації. Відзначено, що загальною рисою електроприводу рейкового транспорту є нелінійна характеристика навантаження. Показано, що за певних комбінацій параметрів у тяговому електроприводі можливі фрикційні автоколивання. Ефективне усунення фрикційних автоколивань за рахунок синтезу системи з нейрорегулятором. Нейронна мережа має три вхідні нейрони, на які подається вектор вхідних сигналів у вигляді сигналу напруги, сигналу значення швидкості двигуна поточного та попереднього значення швидкості енергії. Кількість нейронів прихованого шару системи становить 20 та один вихідний нейрон. На вихідному нейроні формуються керуючі впливу для перетворювача частоти. Нейронні мережі такого типу позначаються NN3-20-1. Для всієї оптимізації параметрів нейронних мереж використовується метод генетичного алгоритму. Імітаційна модель електроприводу рейкового транспорту має інтеграцію блоку комп’ютерного зору. Підвищення рівня автоматизації та безпеки руху рейкових транспортних засобів можливо на основі комп’ютерного зору. Особливістю цієї структури є наявність у ній нейрорегулятора NN. NN забезпечує задану якість перехідного процесу у всьому діапазоні навантажень і при знаходженні робочої точки на спадаючій ділянці. Розроблена система автоматичного регулювання швидкості руху рейкових транспортних засобів залежно від кривизни колії для підвищення рівня автоматизації та безпеки руху. Моделювання системи показало її працездатність, яка проявляється в зниженні швидкості руху рейкових транспортних засобів при русі по ділянці колії з кривизною. Наукова новизна. Ефективне усунення фрикційних автоколивань за рахунок застосування нейрорегулятора. Практична цінність. Розроблена система автоматичного регулювання швидкості рейкового транспорту залежно від кривизни колії для підвищення рівня автоматизації та безпеки руху

Robust visual odometry using uncertainty models

In dense, urban environments, GPS by itself cannot be relied on to provide accurate positioning information. Signal reception issues (e.g. occlusion, multi-path effects) often prevent the GPS receiver from getting a positional lock, causing holes in the absolute positioning data. In order to keep assisting the driver, other sensors are required to track the vehicle motion during these periods of GPS disturbance. In this paper, we propose a novel method to use a single on-board consumer-grade camera to estimate the relative vehicle motion. The method is based on the tracking of ground plane features, taking into account the uncertainty on their backprojection as well as the uncertainty on the vehicle motion. A Hough-like parameter space vote is employed to extract motion parameters from the uncertainty models. The method is easy to calibrate and designed to be robust to outliers and bad feature quality. Preliminary testing shows good accuracy and reliability, with a positional estimate within 2 metres for a 400 metre elapsed distance. The effects of inaccurate calibration are examined using artificial datasets, suggesting a self-calibrating system may be possible in future work

Opportunity rover localization and topographic mapping at the landing site of Meridiani Planum, Mars

This paper presents the results of Mars topographic mapping and lander and rover localization for the Opportunity rover at Meridiani Planum during the Mars Exploration Rover (MER) 2003 mission. By Sol 458, the Opportunity rover traversed a distance of 5.20 km. We localized the lander using two-way Doppler radio positioning and cartographic triangulation of craters visible in both orbital and ground images. Additional high-resolution orbital images were taken to verify the determined lander position. Visual odometry and bundle adjustment techniques were applied to overcome wheel slippages, azimuthal angle drift, and other navigation errors (as large as 21% within Eagle crater). In addition, orbit-to-ground image-based adjustment was applied to correct rover location errors where bundle adjustment was not applicable. We generated timely topographic products, including orthoimages, digital terrain models (DTMs), three-dimensional (3-D) crater models, and rover traverse maps. In particular, detailed 3-D terrain models of major features, such as Endurance crater, have been generated using multisite panoramic stereo images based on bundle adjustment and wide baseline stereo technique

Numerical Efficiency of Inverse Simulation Methods Applied to a Wheeled Rover

Extending the navigational capability of planetary rovers is essential for increasing the scientific outputs from such exploratory missions. In this paper a navigation method based on Inverse Simulation is applied to a four wheel rover. The method calculates the required control inputs to achieve a desired, specified response. Here this is a desired trajectory defined as a series of waypoints. Inverse Simulation considers the complete system dynamics of the rover to calculate the control input using an iterative, numerical Newton - Raphson scheme. The paper provides an insight into the numerical parameters that affect the performance of the method. Also, the influence of varying the timestep and the convergence tolerance is examined in terms of the quality of the calculated control input and the resulting trajectory, as well as the execution time. From this analysis a set of parameters and recommendations to successfully apply Inverse Simulation to a rover is presented