Independent Motion Detection with Event-driven Cameras

Unlike standard cameras that send intensity images at a constant frame rate, event-driven cameras asynchronously report pixel-level brightness changes, offering low latency and high temporal resolution (both in the order of micro-seconds). As such, they have great potential for fast and low power vision algorithms for robots. Visual tracking, for example, is easily achieved even for very fast stimuli, as only moving objects cause brightness changes. However, cameras mounted on a moving robot are typically non-stationary and the same tracking problem becomes confounded by background clutter events due to the robot ego-motion. In this paper, we propose a method for segmenting the motion of an independently moving object for event-driven cameras. Our method detects and tracks corners in the event stream and learns the statistics of their motion as a function of the robot's joint velocities when no independently moving objects are present. During robot operation, independently moving objects are identified by discrepancies between the predicted corner velocities from ego-motion and the measured corner velocities. We validate the algorithm on data collected from the neuromorphic iCub robot. We achieve a precision of ~ 90 % and show that the method is robust to changes in speed of both the head and the target.Comment: 7 pages, 6 figure

Continuous fusion of motion data using an axis-angle rotation representation with uniform B-spline

The fusion of motion data is key in the fields of robotic and automated driving. Most existing approaches are filter-based or pose-graph-based. By using filter-based approaches, parameters should be set very carefully and the motion data can usually only be fused in a time forward direction. Pose-graph-based approaches can fuse data in time forward and backward directions. However, pre-integration is needed by applying measurements from inertial measurement units. Additionally, both approaches only provide discrete fusion results. In this work, we address this problem and present a uniform B-spline-based continuous fusion approach, which can fuse motion measurements from an inertial measurement unit and pose data from other localization systems robustly, accurately and efficiently. In our continuous fusion approach, an axis-angle is applied as our rotation representation method and uniform B-spline as the back-end optimization base. Evaluation results performed on the real world data show that our approach provides accurate, robust and continuous fusion results, which again supports our continuous fusion concept

Map-based localization for urban service mobile robotics

Mobile robotics research is currently interested on exporting autonomous navigation results achieved in indoor environments, to more challenging environments, such as, for instance, urban pedestrian areas. Developing mobile robots with autonomous navigation capabilities in such urban environments supposes a basic requirement for a upperlevel service set that could be provided to an users community. However, exporting indoor techniques to outdoor urban pedestrian scenarios is not evident due to the larger size of the environment, the dynamism of the scene due to pedestrians and other moving obstacles, the sunlight conditions, and the high presence of three dimensional elements such as ramps, steps, curbs or holes. Moreover, GPS-based mobile robot localization has demonstrated insufficient performance for robust long-term navigation in urban environments. One of the key modules within autonomous navigation is localization. If localization supposes an a priori map, even if it is not a complete model of the environment, localization is called map-based. This assumption is realistic since current trends of city councils are on building precise maps of their cities, specially of the most interesting places such as city downtowns. Having robots localized within a map allows for a high-level planning and monitoring, so that robots can achieve goal points expressed on the map, by following in a deliberative way a previously planned route. This thesis deals with the mobile robot map-based localization issue in urban pedestrian areas. The thesis approach uses the particle filter algorithm, a well-known and widely used probabilistic and recursive method for data fusion and state estimation. The main contributions of the thesis are divided on four aspects: (1) long-term experiments of mobile robot 2D and 3D position tracking in real urban pedestrian scenarios within a full autonomous navigation framework, (2) developing a fast and accurate technique to compute on-line range observation models in 3D environments, a basic step required by the real-time performance of the developed particle filter, (3) formulation of a particle filter that integrates asynchronous data streams and (4) a theoretical proposal to solve the global localization problem in an active and cooperative way, defining cooperation as either information sharing among the robots or planning joint actions to solve a common goal.Actualment, la recerca en robòtica mòbil té un interés creixent en exportar els resultats de navegació autònoma aconseguits en entorns interiors cap a d'altres tipus d'entorns més exigents, com, per exemple, les àrees urbanes peatonals. Desenvolupar capacitats de navegació autònoma en aquests entorns urbans és un requisit bàsic per poder proporcionar un conjunt de serveis de més alt nivell a una comunitat d'usuaris. Malgrat tot, exportar les tècniques d'interiors cap a entorns exteriors peatonals no és evident, a causa de la major dimensió de l'entorn, del dinamisme de l'escena provocada pels peatons i per altres obstacles en moviment, de la resposta de certs sensors a la il.luminació natural, i de la constant presència d'elements tridimensionals tals com rampes, escales, voreres o forats. D'altra banda, la localització de robots mòbils basada en GPS ha demostrat uns resultats insuficients de cara a una navegació robusta i de llarga durada en entorns urbans. Una de les peces clau en la navegació autònoma és la localització. En el cas que la localització consideri un mapa conegut a priori, encara que no sigui un model complet de l'entorn, parlem d'una localització basada en un mapa. Aquesta assumpció és realista ja que la tendència actual de les administracions locals és de construir mapes precisos de les ciutats, especialment dels llocs d'interés tals com les zones més cèntriques. El fet de tenir els robots localitzats en un mapa permet una planificació i una monitorització d'alt nivell, i així els robots poden arribar a destinacions indicades sobre el mapa, tot seguint de forma deliberativa una ruta prèviament planificada. Aquesta tesi tracta el tema de la localització de robots mòbils, basada en un mapa i per entorns urbans peatonals. La proposta de la tesi utilitza el filtre de partícules, un mètode probabilístic i recursiu, ben conegut i àmpliament utilitzat per la fusió de dades i l'estimació d'estats. Les principals contribucions de la tesi queden dividides en quatre aspectes: (1) experimentació de llarga durada del seguiment de la posició, tant en 2D com en 3D, d'un robot mòbil en entorns urbans reals, en el context de la navegació autònoma, (2) desenvolupament d'una tècnica ràpida i precisa per calcular en temps d'execució els models d'observació de distàncies en entorns 3D, un requisit bàsic pel rendiment del filtre de partícules a temps real, (3) formulació d'un filtre de partícules que integra conjunts de dades asíncrones i (4) proposta teòrica per solucionar la localització global d'una manera activa i cooperativa, entenent la cooperació com el fet de compartir informació, o bé com el de planificar accions conjuntes per solucionar un objectiu comú

Attention and Anticipation in Fast Visual-Inertial Navigation

We study a Visual-Inertial Navigation (VIN) problem in which a robot needs to estimate its state using an on-board camera and an inertial sensor, without any prior knowledge of the external environment. We consider the case in which the robot can allocate limited resources to VIN, due to tight computational constraints. Therefore, we answer the following question: under limited resources, what are the most relevant visual cues to maximize the performance of visual-inertial navigation? Our approach has four key ingredients. First, it is task-driven, in that the selection of the visual cues is guided by a metric quantifying the VIN performance. Second, it exploits the notion of anticipation, since it uses a simplified model for forward-simulation of robot dynamics, predicting the utility of a set of visual cues over a future time horizon. Third, it is efficient and easy to implement, since it leads to a greedy algorithm for the selection of the most relevant visual cues. Fourth, it provides formal performance guarantees: we leverage submodularity to prove that the greedy selection cannot be far from the optimal (combinatorial) selection. Simulations and real experiments on agile drones show that our approach ensures state-of-the-art VIN performance while maintaining a lean processing time. In the easy scenarios, our approach outperforms appearance-based feature selection in terms of localization errors. In the most challenging scenarios, it enables accurate visual-inertial navigation while appearance-based feature selection fails to track robot's motion during aggressive maneuvers.Comment: 20 pages, 7 figures, 2 table

Event-Based Motion Segmentation by Motion Compensation

In contrast to traditional cameras, whose pixels have a common exposure time, event-based cameras are novel bio-inspired sensors whose pixels work independently and asynchronously output intensity changes (called "events"), with microsecond resolution. Since events are caused by the apparent motion of objects, event-based cameras sample visual information based on the scene dynamics and are, therefore, a more natural fit than traditional cameras to acquire motion, especially at high speeds, where traditional cameras suffer from motion blur. However, distinguishing between events caused by different moving objects and by the camera's ego-motion is a challenging task. We present the first per-event segmentation method for splitting a scene into independently moving objects. Our method jointly estimates the event-object associations (i.e., segmentation) and the motion parameters of the objects (or the background) by maximization of an objective function, which builds upon recent results on event-based motion-compensation. We provide a thorough evaluation of our method on a public dataset, outperforming the state-of-the-art by as much as 10%. We also show the first quantitative evaluation of a segmentation algorithm for event cameras, yielding around 90% accuracy at 4 pixels relative displacement.Comment: When viewed in Acrobat Reader, several of the figures animate. Video: https://youtu.be/0q6ap_OSBA

Space robot simulator vehicle

A Space Robot Simulator Vehicle (SRSV) was constructed to model a free-flying robot capable of doing construction, manipulation and repair work in space. The SRSV is intended as a test bed for development of dynamic and static control methods for space robots. The vehicle is built around a two-foot-diameter air-cushion vehicle that carries batteries, power supplies, gas tanks, computer, reaction jets and radio equipment. It is fitted with one or two two-link manipulators, which may be of many possible designs, including flexible-link versions. Both the vehicle body and its first arm are nearly complete. Inverse dynamic control of the robot's manipulator has been successfully simulated using equations generated by the dynamic simulation package SDEXACT. In this mode, the position of the manipulator tip is controlled not by fixing the vehicle base through thruster operation, but by controlling the manipulator joint torques to achieve the desired tip motion, while allowing for the free motion of the vehicle base. One of the primary goals is to minimize use of the thrusters in favor of intelligent control of the manipulator. Ways to reduce the computational burden of control are described

Why fly blind? Event-based visual guidance for ornithopter robot flight

Under licence Creative Commons - Green Open Access (IEEE).The development of perception and control methods that allow bird-scale flapping-wing robots (a.k.a. ornithopters) to perform autonomously is an under-researched area. This paper presents a fully onboard event-based method for ornithopter robot visual guidance. The method uses event cameras to exploit their fast response and robustness against motion blur in order to feed the ornithopter control loop at high rates (100 Hz). The proposed scheme visually guides the robot using line features extracted in the event image plane and controls the flight by actuating over the horizontal and vertical tail deflections. It has been validated on board a real ornithopter robot with real-time computation in low-cost hardware. The experimental evaluation includes sets of experiments with different maneuvers indoors and outdoors.Consejo Europeo de Investigación (ERC) 78824