Parameter Optimization for Visual Obstacle Detection Using a Derandomized Evolution Strategy

The autonomous mobile robot ARNOLD uses information from a stereo camera system for navigating in an unknown and dynamically changing environment. A method called Inverse Perspective Mapping (IPM) is used for visual obstacle detection. The performance of this algorithm depends on the quality of the internal camera model. In this paper we employ an Evolutionary Algorithm (EA) to improve the parameters of this model. We use a derandomized evolution strategy called (μ/μI , λ)-CMA, which adapts the complete covariance matrix of the mutation distribution. After descriptions of the IPM and the CMA, we show that the proposed optimization method leads to better parameter settings than adjustment by an expert.