To solve the problem of inaccurate localization of model which is caused by inaccurate manual labeling in 3D point cloud training data, a novel robust 3D object detection method based on localization uncertainty was proposed. Firstly, with the 3D voxel grid-based Sparsely Embedded CONvolutional Detection (SECOND) network as basic network, the prediction of localization uncertainty was added based on Region Proposal Network (RPN). Then, during the training process, the localization uncertainty was modeled by using Gaussian and Laplace distribution models, and the localization loss function was redefined. Finally, during the prediction process, the threshold filtering and Non-Maximum Suppression (NMS) methods were performed to filter candidate objects based on the object confidence which was consisted of the localization uncertainty and classification confidence. Experimental results on the KITTI 3D object detection dataset show that compared with SECOND network, the proposed algorithm has the detection accuracy improved by 0.5 percentage points on car category at moderate level. The detection accuracy of the proposed algorithm is 3.1 percentage points higher than that of SECOND network with adding disturbance simulation noise to the training data in the best case. The proposed algorithm improves the accuracy of 3D object detection, which reduces false detection and improves the accuracy of 3D bounding boxes, and is more robust to noisy data.