Engineering topics

Sridhar, Banavar

Publications and source records attributed to Sridhar, Banavar.

At least 109 records · Page 6

NASA NTRS1993

Vision based obstacle detection and grouping for helicopter guidance

Electro-optical sensors can be used to compute range to objects in the flight path of a helicopter. The computation is based on the optical flow/motion at different points in the image. The motion algorithms provide a sparse set of ranges to discrete features in the image sequence as a function of azimuth and elevation. For obstacle avoidance guidance and display purposes, these discrete set of ranges, varying from a few hundreds to several thousands, need to be grouped into sets which correspond to objects in the real world. This paper presents a new method for object segmentation based on clustering the sparse range information provided by motion algorithms together with the spatial relation provided by the static image. The range values are initially grouped into clusters based on depth. Subsequently, the clusters are modified by using the K-means algorithm in the inertial horizontal plane and the minimum spanning tree algorithms in the image plane. The object grouping allows interpolation within a group and enables the creation of dense range maps. Researchers in robotics have used densely scanned sequence of laser range images to build three-dimensional representation of the outside world. Thus, modeling techniques developed for dense range images can be extended to sparse range images. The paper presents object segmentation results for a sequence of flight images.

Sridhar, Banavar↗

NASA NTRS1993

A parallel implementation of a multisensor feature-based range-estimation method

There are many proposed vision based methods to perform obstacle detection and avoidance for autonomous or semi-autonomous vehicles. All methods, however, will require very high processing rates to achieve real time performance. A system capable of supporting autonomous helicopter navigation will need to extract obstacle information from imagery at rates varying from ten frames per second to thirty or more frames per second depending on the vehicle speed. Such a system will need to sustain billions of operations per second. To reach such high processing rates using current technology, a parallel implementation of the obstacle detection/ranging method is required. This paper describes an efficient and flexible parallel implementation of a multisensor feature-based range-estimation algorithm, targeted for helicopter flight, realized on both a distributed-memory and shared-memory parallel computer.

Suorsa, Raymond E.↗

NASA NTRS1992

Vision-based obstacle detection for rotorcraft flight

An obstacle detection approach to rotorcraft flight is described which is based on feature tracking and recursive range estimation. Flight characteristics are taken into account. A range map derived on the basis of this approach provides an advisory display to the pilot and can serve as input to an automatic obstacle-avoidance guidance system. A NASA CH-47 Chinook helicopter was used to develop an image and rotorcraft flight data base for verification of obstacle detection concepts. The performance of the passive range estimation algorithms is demonstrated using both laboratory image and flight image data.

Sridhar, Banavar↗

NASA NTRS1992

Detection And Avoidance Of Obstacles By Helicopters

Report discusses problems relevant to control subsystems enabling helicopters on nap-of-the-Earth flight paths to detect and avoid obstacles automatically. Indicates similarities between this and obstacle-avoidance problem of industrial mobile robots. Two approaches extend two-dimensional obstacle-avoidance concept to three dimensions. First involves direct search of three-dimensional range-map data for indications of openings between obstacles. Second involves compression of data into two-dimensional map for path search.

Cheng, Victor H. L.↗

NASA NTRS1992

Vision-based range estimation using helicopter flight data

Pilot aiding during low-altitude flight depends on the ability to detect and locate obstacles near the helicopter's intended flightpath. Computer-vision-based methods provide one general approach for obstacle detection and range estimation. Several algorithms have been developed for this purpose, but have not been tested with actual flight data. This paper presents results obtained using helicopter flight data with a feature-based range estimation algorithm. A method for recursively estimating range using a Kalman filter with a monocular sequence of images and knowledge of the camera's motion is described. The helicopter flight experiment and four resulting datasets are discussed. Finally the performance of the range estimation algorithm is explored in detail based on comparison of the range estimates with true range measurements collected during the flight experiment.

Smith, Philip N.↗

NASA NTRS1992

Vision-based range estimation using helicopter flight data

Smith, Phillip N.↗

NASA NTRS1992

Analysis of image-based navigation system for rotorcraft low-altitude flight

Some of the issues in the location of objects using a sequence of images from a passive sensor are examined. Image-object differential equations for a rotorcraft executing an arbitrary maneuver are developed. Assuming an onboard inertial navigation system for rotorcraft, state estimation, this study considers how object location is affected by the choice of Kalman filter estimation technique, the rotorcraft, and the object. Simulation results are presented.

Sridhar, Banavar↗

NASA NTRS1992

Status of automatic guidance systems for rotorcraft in low altitude flight

Rotorcraft operating in high-threat environments fly close to the earth's surface to utilize surrounding terrain, vegetation, or man-made objects to minimize the risk of being detected by an enemy. The piloting of the rotorcraft is at best a very demanding task and the pilots need help from on-board automation tools in order to devote more time to mission-related activities. The Automated Nap-of-the-Earth (NOE) Flight Program is a cooperative NASA/Army program aimed at the development of technologies for enhancing piloted low-altitude/NOE flight path management and control through computer and sensor aiding. The long-term objective is to work towards achieving automation for aiding the pilot in NOE flight with a flight demonstration of resulting computer/sensor aiding concepts at an established course. The technology for pilot-centered NOE automation is not currently available. Success in automating NOE functions will depend on major breakthroughs in real-time flight path planning algorithms, effective methods for the pilot to interface to the automatic modes, understanding of visual images, sensor data processing/fusion, and sensor development. Our approach to developing the technologies required to solve this problem consist of the following phases: (1) algorithm development, (2) laboratory evaluation, (3) piloted ground simulation, and (4) evaluation in flight. An overview of the research in this area at NASA Ames Research Center is given.

Sridhar, Banavar↗

NASA NTRS1992

Vision-based range estimation using helicopter flight data

Pilot aiding during low-altitude flight depends on the ability to detect and locate obstacles near the helicopter's intended flightpath. Computer-vision-based methods provide one general approach for obstacle detection and range estimation. Several algorithms have been developed for this purpose, but have not been tested with actual flight data. This paper presents results obtained using helicopter flight data with a feature-based range estimation algorithm. A method for recursively estimating range using a Kalman filter with a monocular sequence of images and knowledge of the camera's motion is described. The helicopter flight experiment and one of four resulting datasets is briefly discussed. Finally the performance of the range estimation algorithm is explored based on comparison of the range estimates with true range measurements collected during the flight experiment.

Smith, Phillip N.↗

NASA NTRS1992

Technologies for automating rotorcraft nap-of-the-earth flight

This paper discusses the technologies required for automating rotorcraft nap-of-the-earth flight, where the use of natural obstacles for masking from the enemy is intentional and the danger of undesirable obstacles such as enemy traps is real. Specifically, the automatic guidance structure is modeled by three decision-making levels: the far-field mission planning and the mid-field terrain-masking trajectory shaping are both driven by prestored terrain data, whereas the nearfield obstacle detection/avoidance is driven by real-time on-board sensor data. This paper summarizes the far-field and mid-field accomplishments, and reports on the status of the more-recent efforts in obstacle detection and avoidance development. Obstacle detection is based primarily on passive imaging sensors for the desirable properties of covertness and wide field of view, although active sensors are included in the structure to provide the much needed high resolution for thin-wire detection.

Cheng, Victor H. L.↗

NASA NTRS1991

Comparison of motion and stereo methods in passive ranging systems

The authors compare the estimates in passive ranging systems using motion and stereo approaches. It is shown that an integrated approach is necessary to provide better range estimates over a field-of-view (FOV) of interest in helicopter flight. The recursive approach for processing a sequence of stereo images, described together with a recursive motion algorithm (RMA), provides the basis for an integrated method to provide more accurate range information. Results based on motion sequences of stereo images are presented.

Sridhar, Banavar↗

NASA NTRS1991

Inertial navigation sensor integrated motion analysis for obstacle detection

The authors describe a maximally passive system for obstacle detection and avoidance designed for rotorcraft which benefits from the use of inertial data. The sensor options are briefly discussed, and sensor fusion needs are mentioned. Some critical system requirements and their effect on system implementation are discussed. In addition, high-level system implementation issues are presented.

Roberts, Barry↗

NASA NTRS1991

Validation of vision-based obstacle detection algorithms for low-altitude helicopter flight

A validation facility being used at the NASA Ames Research Center is described which is aimed at testing vision based obstacle detection and range estimation algorithms suitable for low level helicopter flight. The facility is capable of processing hundreds of frames of calibrated multicamera 6 degree-of-freedom motion image sequencies, generating calibrated multicamera laboratory images using convenient window-based software, and viewing range estimation results from different algorithms along with truth data using powerful window-based visualization software.

Suorsa, Raymond↗

NASA NTRS1991

Passive range sensor refinement using texture and segmentation

Electrooptical sensors provide a covert way of computing range during helicopter flight. The optical flow-based computation of range provides range information only in certain distinguishable parts of the image. The regions where range information is available can be increased by performing texture analysis and object segmentation in the image. This paper reviews some of the literature on texture segmentation methods with a view towards applying them to images containing both man-made and natural objects at varying ranges. Two algorithmic approaches are given and their application to a real image is demonstrated. Results indicate that it will be necessary to combine several different texture measures and methods in a hierarchical way in order to achieve an object segmentation which is useful in enhancing range information.

Sridhar, Banavar↗

NASA NTRS1991

Vision based techniques for rotorcraft low altitude flight

An overview of research in obstacle detection at NASA Ames Research Center is presented. The research applies techniques from computer vision to automation of rotorcraft navigation. The development of a methodology for detecting the range to obstacles based on the maximum utilization of passive sensors is emphasized. The development of a flight and image data base for verification of vision-based algorithms, and a passive ranging methodology tailored to the needs of helicopter flight are discussed. Preliminary results indicate that it is possible to obtain adequate range estimates except at regions close to the FOE. Closer to the FOE, the error in range increases since the magnitude of the disparity gets smaller, resulting in a low SNR.

Sridhar, Banavar↗

NASA NTRS1990

Computer vision techniques for rotorcraft low altitude flight

Rotorcraft operating in high-threat environments fly close to the earth's surface to utilize surrounding terrain, vegetation, or manmade objects to minimize the risk of being detected by an enemy. Increasing levels of concealment are achieved by adopting different tactics during low-altitude flight. Rotorcraft employ three tactics during low-altitude flight: low-level, contour, and nap-of-the-earth (NOE). The key feature distinguishing the NOE mode from the other two modes is that the whole rotorcraft, including the main rotor, is below tree-top whenever possible. This leads to the use of lateral maneuvers for avoiding obstacles, which in fact constitutes the means for concealment. The piloting of the rotorcraft is at best a very demanding task and the pilot will need help from onboard automation tools in order to devote more time to mission-related activities. The development of an automation tool which has the potential to detect obstacles in the rotorcraft flight path, warn the crew, and interact with the guidance system to avoid detected obstacles, presents challenging problems. Research is described which applies techniques from computer vision to automation of rotorcraft navigtion. The effort emphasizes the development of a methodology for detecting the ranges to obstacles in the region of interest based on the maximum utilization of passive sensors. The range map derived from the obstacle-detection approach can be used as obstacle data for the obstacle avoidance in an automatic guidance system and as advisory display to the pilot. The lack of suitable flight imagery data presents a problem in the verification of concepts for obstacle detection. This problem is being addressed by the development of an adequate flight database and by preprocessing of currently available flight imagery. The presentation concludes with some comments on future work and how research in this area relates to the guidance of other autonomous vehicles.

Sridhar, Banavar↗

NASA NTRS1990

Kalman filter based range estimation for autonomous navigation using imaging sensors

Rotorcraft operating in high-threat environments fly close to the surface of the earth to utilize surrounding terrain, vegetation, or man-made objects to minimize the risk of being detected by the enemy. Two basic requirements for obstacle avoidance are detection and range estimation of the object from the current rotorcraft position. There are many approaches to the estimation of range using a sequence of images. The approach used in this analysis differes from previous methods in two significant ways: an attempt is not made to estimate the rotorcraft's motion from the images; and the interest lies in recursive algorithms. The rotorcraft parameters are assumed to be computed using an onboard inertial navigation system. Given a sequence of images, using image-object differential equations, a Kalman filter (Sridhar and Phatak, 1988) can be used to estimate both the relative coordinates and the earth coordinates of the objects on the ground. The Kalman filter can also be used in a predictive mode to track features in the images, leading to a significant reduction of search effort in the feature extraction step of the algorithm. The purpose is to summarize early results obtained in extending the Kalman filter for use with actual image sequences. The experience gained from the application of this algorithm to real images is very valuable and is a necessary step before proceeding to the estimation of range during low-altitude curvilinear flight. A simple recursive method is presented to estimate range to objects using a sequence of images. The method produces good range estimates using real images in a laboratory set up and needs to be evaluated further using several different image sequences to test its robustness. The feature generation part of the algorithm requires further refinement on the strategies to limit the number of features (Sridhar and Phatak, 1989). The extension of the work reported here to curvilinear flight may require the use of the extended Kalman filter.

Sridhar, Banavar↗

NASA NTRS1990

Integration of motion and stereo sensors in passive ranging systems

A recursive approach is described for processing a sequence of stereo images. It will be the basis for an integrated stereo and motion method to provide more accurate range information using a passive ranging system. Results based on motion sequences of stereo images are presented. The approach is also applicable to other autonomous systems and in robotics.

Sridhar, Banavar↗