Vision-based navigation using multi-rate feedback from optic flow and scene reconstruction

Vision-Based Navigation Using Multi-Rate Feedback from Optic Flow and Scene Reconstruction

Distance estimation from visually simulated self-motion is imprecise. Depending on the evaluation method, travelled distance can be under- or over-estimated. One particular method consists of asking a stationary observer, exposed to an immersive optical flow, simulating forward self-motion, to indicate when s/he thinks s/he has reached the remembered position of a previously seen distant target. In this case, subjective evaluation of travelled distance is generally overestimated (i.e., the subject undershoots the target). Recent studies suggest that a translational optical flow with biological additional oscillations (simulating the optical effects of natural locomotion) would increase the sensation of walking and improve spatial perception, as compared to a purely translational optical flow. In the present study we tested this hypothesis, by measuring travelled distance estimation, according to two conditions of visual simulation of forward self-motion, at constant speed, in a CAVE setup: (1) an optical flow simulating pure forward translation (2) an optical flow with added "biological" oscillations, reproducing the optical effects of the natural motion of the head during walking. Our results show that an optical flow containing additional biological information enhances the accuracy of travelled distance estimation. The perceptual advantage provided by the biological oscillations in the optical flow is discussed.

In this paper, investigations of brain activity response from experimental works, based on optic flow and wavelet using near infrared spectroscopy are presented. The outcomes of our research project showed that optical flow is a proper technology for the investigation of brain activity based on near-infrared spectroscopy (NIRS). Our research also showed that the results in the temporal domain, spatial domain, and wavelet domain are coherently supporting the analyses of our experimental results. Our wavelet analysis can obtain the majority of the brain activity for the spatiotemporal distribution. Based on the spatiotemporal analysis, we can clearly observe the existence of the dominant channel (DC), which plays a critical role in the brain activity. The spatial distribution of the cortical activity origin can be described by the hemodynamic response in the cerebral cortex after evoked stimulation using near infrared spectroscopy. Further application is expected in the next research outcomes.

Joint direct estimation of 3D geometry and 3D motion using spatio temporal gradients

The fly visual system, although tiny when compared to the mammalian visual system, can still perform highly sophisticated spatial tasks like collision avoidance, landing on objects, pursuit of prey, etc. Flies outperform human-made autonomous flying systems in solving such spatial tasks by a long way. This is partly due to their ability to perceive and respond to optical flow generated by motion in the environment. They are also known to take actions that alter the image flow on their eyes. Higher level neurons in the fly visual system respond to different types of complex optical flows due to rotation and translation, by pooling information from local motion detectors called the elementary motion detectors (EMDs) in the lower level. In this sense, neuronal responses (spikes) from these optical flow detectors in the fly carry highly encoded signals: a single spike can represent a complex dynamical pattern of movement in the visual field. In this paper, we investigate how such highly encoded signals can be interpreted and utilized in the fly's brain, while solely operating on the encoded internal spike patterns within their brain and no direct external (unencoded) sensory information, i.e. a form of grounding. With a computational model of the optical flow detectors based on those in the fly, we show that specific pattern of action (or coordinated motor output) is the only way that a fly can decode its internal spikes and generate meaningful, relevant behavior based on that.

Uninhabited Aerial Vehicles will require autonomous collisio n detection and avoidance capabilities in order to gain wide acceptance and usage in the National Airspace System. The collision detection and avoidance problem is primarily a motion estimation problem, relying on the accuracy of the sensing, processing, actuation and control algorithms onboard the vehicle. Computer vision is a growing field in robotics and autonomous vehicles, and has made vision a feasible technology for object detection in different platforms. In this work, we investigate the feasibil ity of using optic flow techniques in order to design an altitude hold controller for a fixed wing aircraft flying a nap -of -the -earth trajectory in the presence of variable terrain. Simulated results yield an error of approximately 1.5% in altitude variat ion for steady level flight (approximately 15 cm) for terrain disturbances of maximum amplitude 4 m. Experimental results obtained from flight data using the UIUC UAV platform are used to validate the controller, and sources of error along with potential improvements in equipment and procedures are cited. I. Introduction N essential challenge inherent to the integration of uninhabited aerial vehicles (UAVs) into the National Airspace System (NAS) is the inability to provide adequate safety assurances for th ese vehicles in a verifiable manner. The FAA requires that UAVs be capable of see -and -avoid functionality, unfortunately most solutions to the problem of autonomous surveillance, guidance and navigation are insufficiently scalable, reliable, verifiable or cost -effective. Implementation of safe and reliable object avoidance is crucial for the survival of autonomous vehicles intended to navigate in densely populated environments such as the NAS. In order to meet these stringent requirements, UAVs must be eq uipped with reactive sensing technologies that enable real -time conflict detection and object avoidance. Numerous techniques have been developed in the past few years to optimize the decision making process and solve the path planning problem in real -time , while accounting for aircraft dynamics, obstacle avoidance constraints and safe flight considerations. Regrettably, many of these techniques underestimate the difficulty of accurately sensing a dynamic and imperfect environment in an accurate and practic al manner. Making decisions based on distorted estimations of the surrounding environs is a pitfall that can destabilize even the most robust of path planning algorithms. Presently, state -of -the -art conflict detection and resolution systems employ a combi nation of radar, sonar and laser systems: while reliable, these systems are often prohibitive due to weight, size and equipment cost for certain UAV applications. Video recording devices are another alternative for environmental sensing, and have become standard equipment on many UAVs due to their exploratory nature. Although video images from this source are often intended for secondary mission purposes, such as target tracking and surveillance, they have been shown capable of providing valuable informati on for more important tasks, like collision avoidance. However, the nature of a fixed wing UAV platform brings rigorous safety requirements and constraints to the field of autonomous conflict detection and resolution: limited minimum flying speeds and res tricted maneuverability, thereby making the need for real -time sensing and decision -making paramount for safety purposes. In this paper we investigate the applicability of c omputer vision techniques that have been developed in order to estimate motion from two dimensional images. In the next section we provide a brief discussion of optical flow theory, and section 3 provides a direct implementation of the optical flow theory to an aircraft altitude controlle r. Section 4 provides the setup and simulation of several scenarios used to evaluate the performance of the controller designed in the previous section. Experimental video data taken from actual flight tests is used in order to asses the 1

Additive manufacturing has deeply simplified the production of prototypes by speeding up the realization of complex 3- dimensional objects. A well-designed mechanical part requires perfect knowledge of internal stresses and mechanical behavior of the used material. In this study, we propose a new application for the optical-flow method in the field of extensometry. This method has already proven its capabilities for measuring the displacements and speeds of moving objects in video sequences for many motion tracking applications. The orientation of the fused filament deposited strands influences strongly the behavior of the FDM-manufactured part. In order to study the behavior of a mechanical part as a function of the arrangement of the deposited strands, we produced, using FDM, tensile test specimens by superposing several homogeneous layers. Each of the layers is characterized by a fixed strands orientation. These specimens are then tested on a tensile test machine. Due to the different arrangements of the strands according to the layers, the deformations on the surface of the sample can be complex. A conventional extensometer is then no longer usable. Accordingly, we propose to apply the optical flow method to a video of the test sample surface recorded during a tensile test. We present the experimental setup of the measurement method, the implemented algorithm based on a Mathematica optic flow builtin function and some illustration results. The results show that this technique can be used on real parts subjected to stress in order to reveal weakened areas.

Optical flow based super-resolution: A probabilistic approach

Generation of a sequence of behaviors is necessary for the RoboCup Humanoid league to realize not simply an individual robot performance but also cooperative ones between robots. A typical example task is passing a ball between two humanoids, and the issues are: (1) basic skill decomposition, (2) skill learning, and (3) planning to connect the learned skills. This paper presents three methods for basic skill learning (trapping, approaching to, and kicking a ball) based on optic flow information by which a robot obtains sensorimotor mapping to realize the desired skill, assuming that skill decomposition and planning are given in advance. First, optic flow information of the ball is used to predict the trapping point. Next, the flow information caused by the self-motion is classified into the representative vectors, each of which is connected to motor modules and their parameters. Finally, optical flow for the environment caused by kicking motion is used to predict the ball trajectory after kicking. The experimental results are shown and discussion is given with future issues.

Sensitivity of 8-week-old infants to optical flow specifying the shape of a three-dimensional object was assessed. Infants viewed kinetic random-dot displays that specified three-dimensional cubes. The cubes were identical except for the presence or absence of an interior corner. Half of the infants viewed the full display. The other half viewed the central region of the displays, where the flow specifying the presence or absence of the corner differed. Infants in the full-view condition looked significantly longer to a novel cube than to the familiar cube following habituation. In contrast, infants in the partial-view condition looked equally to the novel and familiar cubes, ruling out the possibility that infants who viewed the full displays merely discriminated differences in motion in the central region of the two displays. These findings suggest that infants as young as 8 weeks perceive three-dimensional object shape from optic flow.

This correspondence derives a formal link between temporally weighted frame differences, or disturbance fields, which carry limited information suitable for motion detection, and the optic flow (OF), which carries richer information on local image motion. We use this link to derive a novel, simple, near-recursive optic flow algorithm based on a recursive-filter formulation. Most quantities involved are computed recursively, using only data from the current and previous frame. We can limit expensive OF calculations to pixels where motion magnitude is sufficiently high using image differences which the algorithm computes anyway. Experimental results with well-known synthetic, ground-truthed test sequences and standard performance metrics indicate good quantitative performance. Tests with real sequences suggest similar or better performance than a well-known, similar algorithm due to Lucas and Kanade (LK).

Several studies have shown that the optic flow serves as a tool for navigation for animals. Flying insects use it to follow paths and avoid obstacles, while in primates it represents an additional input that can improve navigational performance. A neuroinspired architecture for optic flow calculation and decision making, based on the cortical organization of the human brain, has been developed and successfully used as a novel control system for a mobile robot. Navigation in a corridor and obstacle avoidance are achieved relying only on the optic flow. Moreover, the presence of the optic flow improves the performance of the robot in target reaching and obstacle avoidance tasks in a virtual environment, affecting its trajectories as shown by experiments with human subjects.

The use of unmanned aerial vehicle (UAV) has been recognized by the majority of people, and is regarded as a reliable tool to complete some tasks. As for the obstacle avoidance of coaxial rotor UAV (CR-UAV), a flight pattern map (Flight Situation Awareness Map: FSAM) that can sense the flight environment and an integrated control method (Improved Artificial Potential Field: IAPF) based on the common artificial potential field method with appropriate improvements to achieve obstacle avoidance (IAPF). Firstly, we construct the FSAM, which can map the environment information around the UAV on the FSAM. Then, based on the FSAM, the IAPF functions are established to achieve the obstacles avoidance. Cause the artificial potential field (APF) has a characteristic that cannot avoid: the problem of local minima, a rotating potential field is put forward to ensure that the CR-UAV has only one potential equilibrium point at the target point in the environment, and it will improve the ability of CR-UAV to avoid complex obstacles. At the end of this study, through data analysis, the performance of obstacle avoidance and the attitude stability of CR-UAV are good, the simulation results confirm that the approaches proposed in this paper can address the obstacles avoidance successfully.KeywordsCR-UAVImproved Artificial Potential Field (IAPF)Flight Situation Awareness Map (FSAM)Obstacles avoidance

This article presents an obstacle avoidance approach for manipulators based on an obstacle avoidance path planning mechanism. Although the obstacle dealt with in the article is of the cubic type, it can be transformed to the spherical type as shown in the article, which has a simpler analytical description. The obstacle avoidance problem has been formulated in terms of collision avoidance of links rather than points. Link collision avoidance is achieved by continuously controlling the link closest point to the obstacle. For the robot SIR-1 which has an articulated chain, a link can be represented as the line segment defined by the Cartesian position of its two neighboring joints. We implement a simple obstacle avoidance path planning algorithm.

One of the difficulties in estimating optical flow is bias. Correcting the bias using the classical techniques is very difficult. The reason is that knowledge of the error statistics is required, which usually cannot be obtained because of lack of data. In this paper, we present an approach which utilizes color information. Color images do not provide more geometric information than monochromatic images to the estimation of optic flow. They do, however, contain additional statistical information. By utilizing the technique of instrumental variables, bias from multiple noise sources can be robustly corrected without computing the parameters of the noise distribution. Experiments on synthesized and real data demonstrate the efficiency of the algorithm.