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Reliable detection of non-traversable hazards is a key requirement for off-road autonomous navigation. A detailed description of each obstacle detection algorithm and their performance on the surveyed obstacle course is presented in this paper.
A major problem in high-precision teleoperation is the high-resolution presentation of depth information. Stereo television has so far proved to be only a partial solution, due to an inherent trade-off among depth resolution, depth distortion and the alignment of the stereo image pair. Converged cameras can guarantee image alignment but suffer significant depth distortion when configured for high depth resolution. Moving the stereo camera rig to scan the work space further distorts depth. The 'dynamic' (camera-motion induced) depth distortion problem was solved by Diner and Von Sydow (1987), who have quantified the 'static' (camera-configuration induced) depth distortion. In this paper, a stereo image presentation technique which yields aligned images, high depth resolution and low depth distortion is demonstrated, thus solving the trade-off problem.
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A laser-directed ranging system for use in telerobotics applications and other applications involving physically handicapped individuals. The ranging system includes a left and right video camera mounted on a camera platform, and a remotely positioned operator. The position of the camera platform is controlled by three servo motors to orient the roll axis, pitch axis and yaw axis of the video cameras, based upon an operator input such as head motion. A laser is provided between the left and right video camera and is directed by the user to point to a target device. The images produced by the left and right video cameras are processed to eliminate all background images except for the spot created by the laser. This processing is performed by creating a digital image of the target prior to illumination by the laser, and then eliminating common pixels from the subsequent digital image which includes the laser spot. The horizontal disparity between the two processed images is calculated for use in a stereometric ranging analysis from which range is determined.
The next phase of the unmanned ground vehicle development sponsored by OSD calls for intelligent velocity control to support cross-country driving speeds of 10 mph by day and 5 mph by night.
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Micro air vehicles which operate autonomously at low altitude in cluttered environments require a method for on-board obstacle avoidance for safe operation. Prior approaches can be divided between purely reactive approaches, mapping low-level visual features directly to headings to maneuver the vehicle around the obstacle, and deliberative methods that use on-board 3-D sensors to create a 3-D, voxel-based world model, which is then used to generate collision free 3-D trajectories. In this paper, we use forward-looking stereo vision with a large horizontal and vertical field of view and project range from stereo into a novel robot-centered, cylindrical, inverse range map we call an egocylinder. With this implementation we reduce the complexity of our world representation from a 3D map to a 2.5D image space representation, which supports very efficient motion planning and collision-checking. Configuration space expansion is done very efficiently on the egocylinder as an image processing function. Deploying a fast reactive motion planner directly on the configuration space expanded egocylinder image, we demonstrate the effectiveness of this new approach experimentally in an indoor environment.
The recent interest in the use of machine vision for flight vehicle guidance is motivated by the need to automate the nap-of-the-earth flight regime of helicopters. Vision-based stereo ranging problem is cast as an optimal control problem in this paper. A quadratic performance index consisting of the integral of the error between observed image irradiances and those predicted by a Pade approximation of the correspondence hypothesis is then used to define an optimization problem. The necessary conditions for optimality yield a set of linear two-point boundary-value problems. These two-point boundary-value problems are solved in feedback form using a version of the backward sweep method. Application of the ranging algorithm is illustrated using a laboratory image pair.
For real-time stereo vision systems, the standard method for estimating sub-pixel stereo disparity given an initial integer disparity map involves fitting parabolas to a matching cost function aggregated over rectangular windows. This results in a phenomenon known as 'pixel-locking,' which produces artificially-peaked histograms of sub-pixel disparity. These peaks correspond to the introduction of erroneous ripples or waves in the 3D reconstruction of truly Rat surfaces. Since stereo vision is a common input modality for autonomous vehicles, these inaccuracies can pose a problem for safe, reliable navigation. This paper proposes a new method for sub-pixel stereo disparity estimation, based on ideas from Lucas-Kanade tracking and optical flow, which substantially reduces the pixel-locking effect. In addition, it has the ability to correct much larger initial disparity errors than previous approaches and is more general as it applies not only to the ground plane.
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.
Development of unmanned vehicles for local navigation in terrains unstructured by humans is reviewed. Modes of navigation include teleoperation or remote control, computer assisted remote driving (CARD), and semiautonomous navigation (SAN). A first implementation of a CARD system was successfully tested using the Robotic Technology Test Vehicle developed by Jet Propulsion Laboratory. Stereo pictures were transmitted to a remotely located human operator, who performed the sensing, perception, and planning functions of navigation. A computer provided range and angle measurements and the path plan was transmitted to the vehicle which autonomously executed the path. This implementation is to be enhanced by providing passive stereo vision and a reflex control system for autonomously stopping the vehicle if blocked by an obstacle. SAN achievements include implementation of a navigation testbed on a six wheel, three-body articulated rover vehicle, development of SAN algorithms and code, integration of SAN software onto the vehicle, and a successful feasibility demonstration that represents a step forward towards the technology required for long-range exploration of the lunar or Martian surface. The vehicle includes a passive stereo vision system with real-time area-based stereo image correlation, a terrain matcher, a path planner, and a path execution planner.
Robust estimation method is proposed to combine multiple observations and create consistent, accurate, dense Digital Elevation Models (DEMs) from lunar orbital imagery. The NASA Ames Intelligent Robotics Group (IRG) aims to produce higher-quality terrain reconstructions of the Moon from Apollo Metric Camera (AMC) data than is currently possible. In particular, IRG makes use of a stereo vision process, the Ames Stereo Pipeline (ASP), to automatically generate DEMs from consecutive AMC image pairs. However, the DEMs currently produced by the ASP often contain errors and inconsistencies due to image noise, shadows, etc. The proposed method addresses this problem by making use of multiple observations and by considering their goodness of fit to improve both the accuracy and robustness of the estimate. The stepwise regression method is applied to estimate the relaxed weight of each observation.
A stereo correlation method on the object domain is proposed to generate the accurate and dense Digital Elevation Models (DEMs) from lunar orbital imagery. The NASA Ames Intelligent Robotics Group (IRG) aims to produce high-quality terrain reconstructions of the Moon from Apollo Metric Camera (AMC) data. In particular, IRG makes use of a stereo vision process, the Ames Stereo Pipeline (ASP), to automatically generate DEMs from consecutive AMC image pairs. Given camera parameters of an image pair from bundle adjustment in ASP, a correlation window is defined on the terrain with the predefined surface normal of a post rather than image domain. The squared error of back-projected images on the local terrain is minimized with respect to the post elevation. This single dimensional optimization is solved efficiently and improves the accuracy of the elevation estimate.
A prototype twin-camera stereo vision system for autonomous robots has been developed at Goddard Space Flight Center. Standard charge coupled device (CCD) imagers are interfaced with commercial frame buffers and direct memory access to a computer. The overlapping portions of the images are analyzed using photogrammetric techniques to obtain information about the position and orientation of objects in the scene. The camera head consists of two 510 x 492 x 8-bit CCD cameras mounted on individually adjustable mounts. The 16 mm efl lenses are designed for minimum geometric distortion. The cameras can be rotated in the pitch, roll, and yaw (pan angle) directions with respect to their optical axes. Calibration routines have been developed which automatically determine the lens focal lengths and pan angle between the two cameras. The calibration utilizes observations of a calibration structure with known geometry. Test results show the precision attainable is plus or minus 0.8 mm in range at 2 m distance using a camera separation of 171 mm. To demonstrate a task needed on Space Station Freedom, a target structure with a movable I beam was built. The camera head can autonomously direct actuators to dock the I-beam to another one so that they could be bolted together.
The paper presents an advanced 3D imaging system based on a combination of stereo vision and light projection methods.
In January, 2004, the Mars Exploration Rover (MER) mission landed two rovers, Spirit and Opportunity, on the surface of Mars. Several autonomous navigation capabilities were employed in space for the first time in this mission. ]n the Entry, Descent, and Landing (EDL) phase, both landers used a vision system called the, Descent Image Motion Estimation System (DIMES) to estimate horizontal velocity during the last 2000 meters (m) of descent, by tracking features on the ground with a downlooking camera, in order to control retro-rocket firing to reduce horizontal velocity before impact. During surface operations, the rovers navigate autonomously using stereo vision for local terrain mapping and a local, reactive planning algorithm called Grid-based Estimation of Surface Traversability Applied to Local Terrain (GESTALT) for obstacle avoidance. ]n areas of high slip, stereo vision-based visual odometry has been used to estimate rover motion, As of mid-June, Spirit had traversed 3405 m, of which 1253 m were done autonomously; Opportunity had traversed 1264 m, of which 224 m were autonomous. These results have contributed substantially to the success of the mission and paved the way for increased levels of autonomy in future missions.
In this paper, we propose a new fuzzy structural matching scheme for space stereo vision which is based on the fuzzy properties of regions of images and effectively reduces the computational burden in the following low level matching process. Three dimensional distance images of a space truss structural model are estimated using this scheme from stereo images sensed by Charge Coupled Device (CCD) TV cameras.