基于多传感融合的自主发育网络场景识别方法

A Scene Recognition Method of Autonomous Developmental Network Based on Multi-sensor Fusion

现有的场景识别方法准确率低,适应能力不强.为此,将自主发育神经网络应用于机器人场景识别任务,提出了2种将自主发育网络与多传感器融合技术相结合的场景识别方法,即基于加权贝叶斯融合的机器人场景识别方法,以及基于同一自主发育网络架构数据融合的场景识别方法,分别在决策层以及数据层对多传感器信息进行融合,提高了场景识别的准确度,而自主发育网络则提升了识别方法针对各种复杂场景的适应能力.对于所提出的场景识别方法进行了实验测试与分析,证实了其有效性及实用性.此外,由于在同一网络架构下进行数据融合可更高效地利用数据,因此这种方法在场景识别的准确度方面具有更为优越的性能.

Considering the low accuracy and poor adaptability of the existing scene recognition methods,the autonomous developmental neural network is applied to the robot scene recognition task,and two scene recognition methods combining the autonomous developmental network and multi-sensor fusion are proposed,namely,the robot scene recognition method based on weighted Bayesian fusion,and the scene recognition method based on data fusion of the same autonomous developmental network architecture,where the multi-sensor information is fused in the decision-making layer and the data layer,respectively,so as to improve the accuracy of scene recognition.Meanwhile,the autonomous developmental network improves the adaptability of the recognition method for various complex scenes.The proposed scene recognition method is tested and analyzed,which proves its effectiveness and practicability.In addition,the proposed method achieves better accuracy in scene recognition due to more efficient use of collected data through data fusion in the same network architecture.