Abstract: In order to improve the effectiveness of multi-sensor fusion in 3D object detection and improve the accuracy of object detection in the utilization of the feature correlation between the front and back frames, a multi-sensor feature fusion 3D object detection network based on multi frame information was proposed. Firstly, using a feature mapping module based on guidance points to convert the camera perspective features of the image into aerial features, the point cloud features and image features were fused with an adaptive fusion module. Afterwards, utilizing historical frame tracking information, multiple frame features were fused. Finally, a detection head CenterPoint was used to detect the objects and to test the 3D object detection network with a dataset nuScenes and real vehicles. The experimental results show that the network can provide higher accuracy and real-time performance.