Dividing the environment around an autonomous vehicle into regions of coherent motion is essential for intelligent navigation. Without this temporal understanding, navigation is reduced to basic obstacle avoidance, which is inadequate in dynamic settings like busy roads or crowded areas.
The book starts by examining motion estimation from biological, algorithmic, and digital viewpoints. It then presents an algorithm aligned with motion processing models and hardware/software requirements. This algorithm uses the optical flow constraint equation and incorporates range data to address the depth-velocity ambiguity—an important factor for autonomous navigation. The book concludes with a detailed discussion of implementing the algorithm in digital hardware, covering the initial motion processing model, hardware platforms, and the overall system architecture.
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