Abstract：Robots need to identify the type of object, detect the shape and judge the distance to the object. This paper presents an object recognition and localization method that uses binocular information based on the Mask R-CNN model. The Mask R-CNN is used to process the binocular image and complete the bounding box selection, recognition and shape segmentation for each image. Then, the neural network feature is used to match the same object in the binocular images. Finally, the iterative closest point (ICP) method is used to estimate the parallax and calculate the distance according to the obtained object shape. Tests show that the method can process data in near real-time speed with better precision than the traditional disparity map algorithm.
彭秋辰, 宋亦旭. 基于Mask R-CNN的物体识别和定位[J]. 清华大学学报（自然科学版）, 2019, 59(2): 135-141.
PENG Qiuchen, SONG Yixu. Object recognition and localization based on Mask R-CNN. Journal of Tsinghua University(Science and Technology), 2019, 59(2): 135-141.
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2019, Vol. 59 
