Raymond E. Suorsa

Passive range estimation for rotorcraft low-altitude flight

The automation of rotorcraft low-altitude flight presents challenging problems in control, computer vision, and image understanding. A critical element in this problem is the ability to detect and locate obstacles, using on-board sensors, and to modify the nominal trajectory. This requirement is also necessary for the safe landing of an autonomous lander on Mars. This paper examines some of the issues in the location of objects, using a sequence of images from a passive sensor, and describes a Kalman filter approach to estimate range to obstacles. The Kalman filter is also used to track features in the images leading to a significant reduction of search effort in the feature-extraction step of the algorithm. The method can compute range for both straightline and curvilinear motion of the sensor. An experiment is designed in the laboratory to acquire a sequence of images along with the sensor motion parameters under conditions similar to helicopter flight. The paper presents range estimation results using this imagery.

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.<<ETX>>

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. >

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. A system capable of supporting autonomous helicopter navigation will need to extract obstacle information from imagery at rates varying from ten images per second to thirty or more images per second depending on the vehicle speed. This paper describes an efficient and flexible parallel implementation of a multisensor feature-based range-estimation algorithm, targeted for automated helicopter flight. The algorithm can track hundreds of features in multiple image sensors using an extended Kalman filter to estimate the features location in a master sensor coordinate frame. The feature-tracking algorithm has reached relative maturity in the laboratory and is now being ported to several real-time architectures to support autonomous helicopter navigation research. The focus of this paper is not the core theory of the vision algorithm itself, but those aspects of it that affect the method of parallelization. The performance of the parallel algorithm is analyzed, with respect to three load balancing schemes, on both a distributed-memory and shared-memory parallel computer

Vision-based obstacle detection for rotorcraft flight

The ability of rotorcraft to fly at low altitude is hindered by the high pilot workload required to avoid obstacles. The development of automation tools that can detect obstacles in the rotorcraft flight path, warn the crew, and interact with the guidance system to avoid detected obstacles would significantly reduce pilot workload and increase safety. This article describes an obstacle detection approach based on feature tracking and recursive range estimation that takes into account the characteristics of rotorcraft flight. The merits and weaknesses of the approach are discussed using image sequences from the laboratory and from flight.

Clustering methods for removing outliers from vision-based range estimates

The automation of rotorcraft low-altitude flight presents challenging problems in flight control and sensor systems. The currently explored approach uses one or more passive sensors, such as a television camera, to extract environmental obstacle information. Obstacle imagery can be processed using a variety of computer vision techniques to produce a time-varying map of range to obstacles in the sensors field of view along the helicopter flight path. To maneuver in tight space, obstacle-avoidance methods would need very reliable range map information by which to guide the helicopter through the environment. In general, most low level computer vision techniques generate sparse range maps which include at least a small percentage of bad estimates (outliers). This paper examines two related techniques which can be used to eliminate outliers from a sparse range map. Each method clusters sparse range map information into different spatial classes relying on a segmented and labeled image to help in spatial classification within the image plane.

Multirate and event driven Kalman filters for helicopter passive ranging

The use of passive range estimation methods for helicopters equipped with a single camera and an inertial navigation system is described. It is shown that the same level of estimation accuracy can be maintained by using multirate and event-driven Kalman filters as with a standard Kalman filter, resulting in a substantial reduction in the amount of computation. These results are based on helicopter flight test images. The actual savings will depend on the details of the implementation.<<ETX>>

Real-time computational needs of a multisensor feature-based range estimation method

The computer vision literature describes many methods to perform obstacle detection and avoidance for autonomous or semi-autonomous vehicles. Methods may be broadly categorized into field-based techniques and feature-based techniques. Field-based techniques have the advantage of regular computational structure at every pixel throughout the image plane. Feature-based techniques are much more data driven in that computational complexity increases dramatically in regions of the image populated by features. It is widely believed that to run computer vision algorithms in real time a parallel architecture is necessary. Field-based techniques lend themselves to easy parallelization due to their regular computational needs. However, we have found that field-based methods are sensitive to noise and have traditionally been difficult to generalize to arbitrary vehicle motion. Therefore, we have sought techniques to parallelize feature-based methods. This paper describes the computational needs of a parallel feature-based range-estimation method developed by NASA Ames. Issues of processing-element performance, load balancing, and data-flow bandwidth are addressed along with a performance review of two architectures on which the feature-based method has been implemented.