elicopters flying at low altitude need information about H objects in the vicinity of their flight path for automatic obstacle avoidance in a guidance system or as a display to warn the pilot. NASA is examining the role of a vision based obstacle detection system to provide a range map, i.e., information about objects as a function of azimuth and elevation. 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

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... les. The computation of a range map for a typical scene may involve several hundred Kalman filters. Optical flow computations provide the measurements Presented at the First IEEE Conference on Control Applications, Dayton, OH, September 13-16, 1992. BSricihar; P Smith, and R.Suorsa are with NASA Ames Research Center; Moffett Field, CA 94035. B. Hussien is M.ith Sterliilg Federal Sxstems Group, Palo Alto, CA 94303. for each Kalman filter. The magnitude of the optical flow 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 is presented here, based on accepting the measurement only under certain conditions. The estimation results based on the standard Kalman filter are compared with results from a multirate Kalman filter and an event-driven Kalman filter on a sequence of helicopter flight images. Results show that, compared to the standard Kalman filter, either the multirate or event-driven formulation can be used to achieve a more uniform estimation accuracy over a large portion of the image with a substantial reduction in the amount of computation. 26 0272-170X/93/SO3.000 1993IEEE IEEE Control Systems