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Real-time vision-based vehicle navigation tasks are typically computationally intensive and significantly complex in terms of resources used. Moreover, mobile applications of navigation systems place severe constraints on their size and power consumption. These constraints can be satisfied by using parallel image processing architectures and parallel control algorithms implemented with analog VLSI circuits. In this paper we present two examples of neuromorphic VLSI sensors that process motion signals to provide continuous time analog output signals that can be useful for real-time navigation tasks. Neuromorphic analog VLSI vision sensors are typically compact, low-power devices that implement parallel architectures to perform in real-time, image (pre)processing operations and provide results that would otherwise require CPU intensive computations [5]. The two neuromorphic sensors we propose in this paper measure respectively the scene’s 2D optical flow field and the target’s position and its direction of motion in one dimension.

Neuromorphic vision sensors for mobile robots