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Results for “MONOCULAR VISION”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

A smart telerobotic system driven by monocular vision

A robotic system that accepts autonomously generated motion and control commands is described. The system provides images from the monocular vision of a camera mounted on a robot's end effector, eliminating the need for traditional guidance targets that must be predetermined and specifically identified. The telerobotic vision system presents different views of the targeted object relative to the camera, based on a single camera image and knowledge of the target's solid geometry.

Defigueiredo, R. J. P.↗
NASA NTRS2011

Autonomous Landing and Ingress of Micro-Air-Vehicles in Urban Environments Based on Monocular Vision

Unmanned micro air vehicles (MAVs) will play an important role in future reconnaissance and search and rescue applications. In order to conduct persistent surveillance and to conserve energy, MAVs need the ability to land, and they need the ability to enter (ingress) buildings and other structures to conduct reconnaissance. To be safe and practical under a wide range of environmental conditions, landing and ingress maneuvers must be autonomous, using real-time, onboard sensor feedback. To address these key behaviors, we present a novel method for vision-based autonomous MAV landing and ingress using a single camera for two urban scenarios: landing on an elevated surface, representative of a rooftop, and ingress through a rectangular opening, representative of a door or window. Real-world scenarios will not include special navigation markers, so we rely on tracking arbitrary scene features; however, we do currently exploit planarity of the scene. Our vision system uses a planar homography decomposition to detect navigation targets and to produce approach waypoints as inputs to the vehicle control algorithm. Scene perception, planning, and control run onboard in real-time; at present we obtain aircraft position knowledge from an external motion capture system, but we expect to replace this in the near future with a fully self-contained, onboard, vision-aided state estimation algorithm. We demonstrate autonomous vision-based landing and ingress target detection with two different quadrotor MAV platforms. To our knowledge, this is the first demonstration of onboard, vision-based autonomous landing and ingress algorithms that do not use special purpose scene markers to identify the destination.

micro air vehicles (MAVs)↗
NASA NTRS2021

Collaborative Pose Estimation of An Unknown Target Using Multiple Spacecraft

A reliable method for pose estimation of an unknown and uncooperative space target using monocular vision remains an open problem. Vision-based pose determination can be challenging in case of unfavorable illumination, time-varying conditions due to rotational motion and relative orbit, and scale ambiguity resolution. To address these challenges, we propose a novel collaborative pose determination algorithm called Multi- Spacecraft Simultaneous Estimation of Pose and Shape algorithm or M-SEPS.Within M-SEPS, a team of chaser spacecraft, each equipped with a monocular camera, exchange information over a local network to jointly estimate the relative kinematic state of the target and its sparse shape landmarks. In this approach, each spacecraft processes its own images and observes particular target landmarks in parallel and in a distributed fashion. Then, the local network is exploited by the spacecraft to share their consensus proposals and aggregate them to achieve the joint estimate. We validate our algorithm using simulations of relative orbits and observations, captured by each chaser spacecraft. To the best of the authors’ knowledge, this is the first cooperative, vision-based algorithm for estimating the pose and shape of a space object for an arbitrary number of spacecraft.

Chung, Soon-Jo↗
NASA NTRS1979

Oculogravic illusion in response to straight-ahead acceleration of a CF-104 aircraft

Experimental subjects wore goggles that restricted monocular vision to a luminous line fixed relative to the head, and they were exposed on one occasion to a straight-ahead acceleration of an aircraft and on another occasion to a tilting chair. The magnitude of change of direction of the resultant acceleration was the same on both occasions, but the perceived movement of the luminous line from the two stimuli was very different. In response to the aircraft stimulus, the oculogravic illusion was experienced and the luminous line was perceived as tilting relative to the subject, in response to the tilting chair stimulus, the line was perceived as remaining fixed relative to the subject. It was concluded that the oculogravic illusion, as experienced in the aircraft (and previously in centrifuges), is a true illusion and not merely a fact of physics.

Graybiel, A.↗
NASA NTRS2001

Maximally Informative Statistics for Localization and Mapping

This paper presents an algorithm for localization and mapping for a mobile robot using monocular vision and odometry as its means of sensing. The approach uses the Variable State Dimension filtering (VSDF) framework to combine aspects of Extended Kalman filtering and nonlinear batch optimization. This paper describes two primary improvements to the VSDF. The first is to use an interpolation scheme based on Gaussian quadrature to linearize measurements rather than relying on analytic Jacobians. The second is to replace the inverse covariance matrix in the VSDF with its Cholesky factor to improve the computational complexity. Results of applying the filter to the problem of localization and mapping with omnidirectional vision are presented.

Deans, Matthew C.↗
NASA NTRS2016

Localization from Visual Landmarks on a Free-Flying Robot

We present the localization approach for Astrobee, a new free-flying robot designed to navigate autonomously on the International Space Station (ISS). Astrobee will accommodate a variety of payloads and enable guest scientists to run experiments in zero-g, as well as assist astronauts and ground controllers. Astrobee will replace the SPHERES robots which currently operate on the ISS, whose use of fixed ultrasonic beacons for localization limits them to work in a 2 meter cube. Astrobee localizes with monocular vision and an IMU, without any environmental modifications. Visual features detected on a pre-built map, optical flow information, and IMU readings are all integrated into an extended Kalman filter (EKF) to estimate the robot pose. We introduce several modifications to the filter to make it more robust to noise, and extensively evaluate the localization algorithm.

Coltin, Brian↗
NASA NTRS2016

Localization from Visual Landmarks on a Free-Flying Robot

We present the localization approach for Astrobee,a new free-flying robot designed to navigate autonomously on board the International Space Station (ISS). Astrobee will conduct experiments in microgravity, as well as assisst astronauts and ground controllers. Astrobee replaces the SPHERES robots which currently operate on the ISS, which were limited to operating in a small cube since their localization system relied on triangulation from ultrasonic transmitters. Astrobee localizes with only monocular vision and an IMU, enabling it to traverse the entire US segment of the station. Features detected on a previously-built map, optical flow information,and IMU readings are all integrated into an extended Kalman filter (EKF) to estimate the robot pose. We introduce several modifications to the filter to make it more robust to noise.Finally, we extensively evaluate the behavior of the filter on atwo-dimensional testing surface.

Coltin, Brian↗
NASA NTRS2008

Automatic Hazard Detection for Landers

Unmanned planetary landers to date have landed 'blind'; that is, without the benefit of onboard landing hazard detection and avoidance systems. This constrains landing site selection to very benign terrain,which in turn constrains the scientific agenda of missions. The state of the art Entry, Descent, and Landing (EDL) technology can land a spacecraft on Mars somewhere within a 20-100km landing ellipse.Landing ellipses are very likely to contain hazards such as craters, discontinuities, steep slopes, and large rocks, than can cause mission-fatal damage. We briefly review sensor options for landing hazard detection and identify a perception approach based on stereo vision and shadow analysis that addresses the broadest set of missions. Our approach fuses stereo vision and monocular shadow-based rock detection to maximize spacecraft safety. We summarize performance models for slope estimation and rock detection within this approach and validate those models experimentally. Instantiating our model of rock detection reliability for Mars predicts that this approach can reduce the probability of failed landing by at least a factor of 4 in any given terrain. We also describe a rock detector/mapper applied to large-high-resolution images from the Mars Reconnaissance Orbiter (MRO) for landing site characterization and selection for Mars missions.

hazard detection↗
NASA NTRS1967

Flight research program. VI.

Correlation between heart rate, landing error and field of view for binocular and monocular sphere of vision of jet pilots

VISUAL FIELD↗
NASA NTRS2012

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.

Swank, Aaron J.↗
NASA NTRS1973

Landing performance by low-time private pilots after the sudden loss of binocular vision - Cyclops II

Study of low-time general aviation pilots, who, in a series of spot landings, were suddenly deprived of binocular vision by patching either eye on the downwind leg of a standard, closed traffic pattern. Data collected during these landings were compared with control data from landings flown with normal vision during the same flight. The sequence of patching and the mix of control and monocular landings were randomized to minimize the effect of learning. No decrease in performance was observed during landings with vision restricted to one eye, in fact, performance improved. This observation is reported at a high level of confidence (p less than 0.001). These findings confirm the previous work of Lewis and Krier and have important implications with regard to aeromedical certification standards.

Lewis, C. E., Jr.↗
NASA NTRS1991

Fusion of monocular cues to detect man-made structures in aerial imagery

The extraction of buildings from aerial imagery is a complex problem for automated computer vision. It requires locating regions in a scene that possess properties distinguishing them as man-made objects as opposed to naturally occurring terrain features. It is reasonable to assume that no single detection method can correctly delineate or verify buildings in every scene. A cooperative-methods paradigm is useful in approaching the building extraction problem. Using this paradigm, each extraction technique provides information which can be added or assimilated into an overall interpretation of the scene. Thus, the main objective is to explore the development of computer vision system that integrates the results of various scene analysis techniques into an accurate and robust interpretation of the underlying three dimensional scene. The problem of building hypothesis fusion in aerial imagery is discussed. Building extraction techniques are briefly surveyed, including four building extraction, verification, and clustering systems. A method for fusing the symbolic data generated by these systems is described, and applied to monocular image and stereo image data sets. Evaluation methods for the fusion results are described, and the fusion results are analyzed using these methods.

Shufelt, Jefferey↗
NASA NTRS1983

Stereo imaging with spaceborne radars

Stereo viewing is a valuable tool in photointerpretation and is used for the quantitative reconstruction of the three dimensional shape of a topographical surface. Stereo viewing refers to a visual perception of space by presenting an overlapping image pair to an observer so that a three dimensional model is formed in the brain. Some of the observer's function is performed by machine correlation of the overlapping images - so called automated stereo correlation. The direct perception of space with two eyes is often called natural binocular vision; techniques of generating three dimensional models of the surface from two sets of monocular image measurements is the topic of stereology.

Leberl, F.↗
NASA NTRS1991

Vision-guided gripping of a cylinder

The motivation for vision-guided servoing is taken from tasks in automated or telerobotic space assembly and construction. Vision-guided servoing requires the ability to perform rapid pose estimates and provide predictive feature tracking. Monocular information from a gripper-mounted camera is used to servo the gripper to grasp a cylinder. The procedure is divided into recognition and servo phases. The recognition stage verifies the presence of a cylinder in the camera field of view. Then an initial pose estimate is computed and uncluttered scan regions are selected. The servo phase processes only the selected scan regions of the image. Given the knowledge, from the recognition phase, that there is a cylinder in the image and knowing the radius of the cylinder, 4 of the 6 pose parameters can be estimated with minimal computation. The relative motion of the cylinder is obtained by using the current pose and prior pose estimates. The motion information is then used to generate a predictive feature-based trajectory for the path of the gripper.

Nicewarner, Keith E.↗
NASA NTRS1992

A rotorcraft flight database for validation of vision-based ranging algorithms

A helicopter flight test experiment was conducted at the NASA Ames Research Center to obtain a database consisting of video imagery and accurate measurements of camera motion, camera calibration parameters, and true range information. The database was developed to allow verification of monocular passive range estimation algorithms for use in the autonomous navigation of rotorcraft during low altitude flight. The helicopter flight experiment is briefly described. Four data sets representative of the different helicopter maneuvers and the visual scenery encountered during the flight test are presented. These data sets will be made available to researchers in the computer vision community.

Smith, Phillip N.↗
NASA NTRS1992

Beyond the cockpit: The visual world as a flight instrument

The use of cockpit instruments to guide flight control is not always an option (e.g., low level rotorcraft flight). Under such circumstances the pilot must use out-the-window information for control and navigation. Thus it is important to determine the basis of visually guided flight for several reasons: (1) to guide the design and construction of the visual displays used in training simulators; (2) to allow modeling of visibility restrictions brought about by weather, cockpit constraints, or distortions introduced by sensor systems; and (3) to aid in the development of displays that augment the cockpit window scene and are compatible with the pilot's visual extraction of information from the visual scene. The authors are actively pursuing these questions. We have on-going studies using both low-cost, lower fidelity flight simulators, and state-of-the-art helicopter simulation research facilities. Research results will be presented on: (1) the important visual scene information used in altitude and speed control; (2) the utility of monocular, stereo, and hyperstereo cues for the control of flight; (3) perceptual effects due to the differences between normal unaided daylight vision, and that made available by various night vision devices (e.g., light intensifying goggles and infra-red sensor displays); and (4) the utility of advanced contact displays in which instrument information is made part of the visual scene, as on a 'scene linked' head-up display (e.g., displaying altimeter information on a virtual billboard located on the ground).

Johnson, W. W.↗
NASA NTRS2019

Fusion of Visible and Thermal-Infrared Imagery for SLAM for Landing on Icy Moons

This paper addresses the problem of localization for landing on the surface of icy moons, like Europa or Enceladus. Due to the possibility of specular reflection as well as high bulk albedo, icy surfaces present new challenges that make traditional vision-based navigation systems relying on visible imagery unreliable. We propose augmenting visible light cameras with a thermal-infrared camera using inverse-depth parameterized monocular EKF-SLAM to address problems arising from the appearance of icy moons. Results were obtained from a novel procedural Europa surface simulation which models the appearance and the thermal properties simultaneously from physically-based methods. In this framework, we show that thermal features improve localization by 23% on average when compared to a visible camera. Moreover, fusing both sensing modalities increases the improvement in localization to 31% on average, compared to using a visible light camera alone.

thermal vision↗