关键词: 智能机器人, 物体检测, 深度学习, R-CNN算法, 深度图像

Abstract: Object detection is a fundamental problem for mobile robots working under indoor environments. Object detection is affected by dynamic environmental changes, especially illumination variation. Indoor illumination change is analyzed so as to research how to identify illumination condition in the environment rapidly from image space feature, and then switch object detection mode according to illumination state which means either visual or laser sensor will be picked, together with feature selection ability provided by deep learning, to guarantee object detection peformance. First, by extracting statistic feautures on Y component in CIEXYZ space of the image together with some other features, the illumination state of environment when getting image is estimtaed rapidly. If the illuminaiton state is appropriate, the R-CNN algorithm will be adopted to finish rapid object detection in image space. In poor or excessive illumination state, the point cloud from 3D laser sensor is tranformed into depth image which is then used by R-CNN to implement object detection. The experimental results verify the validity of the algorithm proposed by this paper.

Key words: intelligent robot, object detection, deep learning, Region-based Convolutional Neural Networks（R-CNN） algorithm, depth map