Local Environment Recognition System Using Modified SURF-Based 3D Panoramic Environment Map for Obstacle Avoidance of a Humanoid Robot

Abstract

This paper addresses a local environment recognition system for obstacle avoidance. In vision systems, obstacles that are located beyond the Field of View (FOV) cannot be detected precisely. To deal with the FOV problem, we propose a 3D Panoramic Environment Map (PEM) using a Modified SURF algorithm (MSURF). Moreover, in order to decide the avoidance direction and motion automatically, we also propose a Complexity Measure (CM) and Fuzzy-Logic-based Avoidance Motion Selector (FL-AMS). The CM is utilized to decide an avoidance direction for obstacles. The avoidance motion is determined using FL-AMS, which considers environmental conditions such as the size of obstacles and available space. The proposed system is applied to a humanoid robot built by the authors. The results of the experiment show that the proposed method can be effectively applied to a practical environment.