We present a scheme for obstacle detection from optical flow which is based on strategies of biological information processing. Optical flow is established by a local "voting" (non-maximum suppression) over the outputs of correlation-type motion detectors similar to those found in the fly visual system. The computational theory of obstacle detection is discussed in terms of space-variances of the motion field. An efficient mechanism for the detection of disturbances in the expected motion field is based on "inverse perspective mapping", i.e., a coordinate transform or retinotopic mapping applied to the image. It turns out that besides obstacle detection, inverse perspective mapping has additional advantages for regularizing optical flow algorithms. Psychophysical evidence for body-scaled obstacle detection and related neurophysiological results are discussed.