场景识别是移动机器人实现拓扑导航的关键。针对未知环境，提出一种基于视觉局部显著区域的自然场景识别方法。首先，提出带反馈的显著性检测模型(FSDM)自底向上进行图像分析;然后，根据显著位置，基于分形实现自动尺度选择，以构造合适尺寸的局部显著区域。对场景图像中的显著区域采用梯度方向、二阶不变矩、归一化色调3种特征进行不变性表示，并根据其匹配率实现场景识别。实验结果表明，FSDM具有较高的显著性检测精度。而且室内室外环境的多次场景识别实验也表明，该方法与全局外观方法相比能够更好地容忍尺度、视角等变化引起的差异，静态场景识别具有较高的准确性。

Scene recognition is a key problem in mobile robot topological navigation. For unknown environments, a natural scene recognition approach based on visual local salient regions is presented. Firstly, a feedback saliency detection model (FSDM) is presented to carry out bottom up scene image analysis. Then, according to the salient positions, automatic scale selection is realized based on fractal dimension to build the local salient regions with appropriate size. Those salient regions are represented by 3 invariant features of gradient orientation, moment and canonical hue. They are used for scene recognition in terms of their match ratio. Experiments show that FSDM can obtain higher accuracy. The scene recognition experiments in both indoor and outdoor environments show that the approach has high stability and tolerance compared to the method based on global appearance when scale and viewpoint etc changed. The accuracy of recognition for static scene is higher.