Suorsa, Raymond E. | Search NASA

NASA NTRS1994

Parallel Processing Systems for Passive Ranging During Helicopter Flight

The complexity of rotorcraft missions involving operations close to the ground result in high pilot workload. In order to allow a pilot time to perform mission-oriented tasks, sensor-aiding and automation of some of the guidance and control functions are highly desirable. Images from an electro-optical sensor provide a covert way of detecting objects in the flight path of a low-flying helicopter. Passive ranging consists of processing a sequence of images using techniques based on optical low computation and recursive estimation. The passive ranging algorithm has to extract obstacle information from imagery at rates varying from five to thirty or more frames per second depending on the helicopter speed. We have implemented and tested the passive ranging algorithm off-line using helicopter-collected images. However, the real-time data and computation requirements of the algorithm are beyond the capability of any off-the-shelf microprocessor or digital signal processor. This paper describes the computational requirements of the algorithm and uses parallel processing technology to meet these requirements. Various issues in the selection of a parallel processing architecture are discussed and four different computer architectures are evaluated regarding their suitability to process the algorithm in real-time. Based on this evaluation, we conclude that real-time passive ranging is a realistic goal and can be achieved with a short time.

Sridhar, Bavavar↗

NASA NTRS1993

Multiple-camera/motion stereoscopy for range estimation in helicopter flight

Aiding the pilot to improve safety and reduce pilot workload by detecting obstacles and planning obstacle-free flight paths during low-altitude helicopter flight is desirable. Computer vision techniques provide an attractive method of obstacle detection and range estimation for objects within a large field of view ahead of the helicopter. Previous research has had considerable success by using an image sequence from a single moving camera to solving this problem. The major limitations of single camera approaches are that no range information can be obtained near the instantaneous direction of motion or in the absence of motion. These limitations can be overcome through the use of multiple cameras. This paper presents a hybrid motion/stereo algorithm which allows range refinement through recursive range estimation while avoiding loss of range information in the direction of travel. A feature-based approach is used to track objects between image frames. An extended Kalman filter combines knowledge of the camera motion and measurements of a feature's image location to recursively estimate the feature's range and to predict its location in future images. Performance of the algorithm will be illustrated using an image sequence, motion information, and independent range measurements from a low-altitude helicopter flight experiment.

Smith, Phillip N.↗

NASA NTRS1993

Multirate and event-driven Kalman filters for helicopter flight

A vision-based obstacle detection system that provides information about objects as a function of azimuth and elevation is discussed. The range map is computed using a sequence of images from a passive sensor, and an extended Kalman filter is used to estimate range to obstacles. The magnitude of the optical flow that provides measurements for each Kalman filter varies significantly over the image depending on the helicopter motion and object location. In a standard Kalman filter, the measurement update takes place at fixed intervals. It may be necessary to use a different measurement update rate in different parts of the image in order to maintain the same signal to noise ratio in the optical flow calculations. A range estimation scheme that accepts the measurement only under certain conditions is presented. The estimation results from the standard Kalman filter are compared with results from a multirate Kalman filter and an event-driven Kalman filter for a sequence of helicopter 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 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.↗