Dynamic Scene Understanding for Autonomous Mobile Robots

Abstract

An approach to the dynamic scene analysis is presented which departs from previous work by emphasizing a qualitative strategy of reasoning and modeling. Instead of refining a single quantitative description of the observed environment over time, multiple qualitative interpretations are maintained simultaneously. This offers superior robustness and flexibility over traditional numerical techniques which are often ill-conditioned and noise-sensitive. The main tasks of the authors' approach are: (1) detect and classify the motion of individual objects in the scene; (2) estimate the robot's egomotion; and (3) derive the 3-D structure of the stationary environment. These three tasks strongly depend on each other. First, the direction of heading (i.e. translation) and rotation of the robot are estimated with respect to stationary locations in the scene. The focus of expansion (FOE) is not determined as particular image location, but as a region of possible FOE-locations called the fuzzy FOE. From this information, a rule-based system constructs and maintains a qualitative scene model. Results of this approach for real and synthetic imagery are presented. >