Robot detection and localization based on deep learning

Abstract

Real-time and accurate robot detection and localization is important for the RoboCup Middle size League (MSL) soccer robots. In the current robot detection methods used by most of the teams, the black-color-based information is used to distinguish robots from the environment, which is not robust if the robot changes its makers' color according to the current rule. Considering the good performance of deep learning on the feature extraction and object detection, in this paper, we propose a novel approach for robot detection and localization based on Convolutional Neural Networks (CNNs) for RoboCup MSL soccer robots. The approach is composed of two stages: robot detection using the RGB image, and robot localization using the depth point cloud. The high accuracy and mean average precision (mAP) verify that the proposed method is suitable for robot detection during the MSL competition, which will benifit the following strategy design and obstacle avoidance procedures. The proposed approach can be easily exploited to deal with different objects and adapted to be used in other RoboCup leagues. The acquired dataset is made available for the community.