Key Laboratory of Instrumentation Science and Dynamic Measurement, Ministry of Education, School of Instrument and Electronics, North University of China, Taiyuan 030051, People's Republic of China.
School of Instrumentation Sciences and Engineering, Southeast University, Nanjing 210096, People's Republic of China.
Rev Sci Instrum. 2020 Dec 1;91(12):125110. doi: 10.1063/5.0027767.
Navigation in primates is generally supported by cognitive maps. Such a map endows an animal with navigational planning capabilities. Numerous methods have been proposed to mimic these natural navigation capabilities in artificial systems. Based on self-navigation and learning strategies in animals, we propose in this work a place recognition strategy for brain-inspired navigation. First, a place recognition algorithm structure based on convolutional neural networks (CNNs) is introduced, which can be applied in the field of intelligent navigation. Second, sufficient images are captured at each landmark and then stored as a reference image library. Simple linear iterative clustering (SLIC) is used to segment each image into superpixels with multi-scale viewpoint-invariant landmarks. Third, highly representative appearance-independent features are extracted from these landmarks through CNNs. In addition, spatial pyramid pooling (SPP) layers are introduced to generate a fixed-length CNN representation, regardless of the image size. This representation boosts the quality of the extracted landmark features. The proposed SLIC-SPP-CNN place recognition algorithm is evaluated on one collected dataset and two public datasets with viewpoint and appearance variations.
灵长类动物的导航通常依赖于认知地图。这种地图赋予了动物进行导航规划的能力。已经提出了许多方法来在人工系统中模拟这些自然的导航能力。基于动物的自导航和学习策略，我们在这项工作中提出了一种基于大脑启发的导航的位置识别策略。首先，引入了一种基于卷积神经网络 (CNN) 的位置识别算法结构，该结构可应用于智能导航领域。其次，在每个地标处拍摄足够的图像，然后将其存储为参考图像库。简单线性迭代聚类 (SLIC) 用于将每个图像分割成具有多尺度不变视点的超像素。第三，通过 CNN 从这些地标中提取出高度代表性的外观独立特征。此外，引入了空间金字塔池化 (SPP) 层，以生成固定长度的 CNN 表示，而与图像大小无关。这种表示提高了提取的地标特征的质量。在所收集的一个数据集和两个具有视点和外观变化的公共数据集上评估了所提出的 SLIC-SPP-CNN 位置识别算法。
