未知环境中基于视觉显著性的自然路标检测

Visual Saliency Based Natural Landmarks Detection under Unknown Environments

自然路标检测是移动机器人在未知环境中表示与识别环境的基础,基于此,本文提出一种具有适应能力、基于视觉显著性的自然路标检测系统.设计了保细节采样策略.能够自行调节参数以适应各种环境纹理分析的 Ga-bor 滤波器,在多尺度空间上计算颜色、纹理的对比度,经综合处理得到描述路标可选区域的显著性指示图.实验结果表明本文算法在显著点检测方面具有较高的准确性,并且具有较高的重复检测能力,能够适应远近尺度、旋转和视角变化等自然环境识别要求.

Natural landmark detection is a basis of mobile robots navigation to represent and recognize unknown environments. A saliency based adaptive natural landmarks detecting system is presented in this paper. Firstly the detail preserving sampling scheme is designed to create multl-scale image spaces where opponencies of color and texture are computed. And the Gabor filter, which can adjust parameters adaptively , is designed to analyze texture of all kinds of environments . At last the saliency map that points out where can be treated as natural landmark is created. Experiments show that this algorithm has better precision on detecting salient points and better repeatability including scale, rotation and viewpoint invariance.