Object-level data association and pose estimation play a fundamental role in semantic SLAM, which remain unsolved due to the lack of robust and accurate algorithms. In this work, we propose an ensemble data associate strategy for integrating the parametric and nonparametric statistic tests. By exploiting the nature of different statistics, our method can effectively aggregate the information of different measurements, and thus significantly improve the robustness and accuracy of data association. We then present an accurate object pose estimation framework, in which an outliers-robust centroid and scale estimation algorithm and an object pose initialization algorithm are developed to help improve the optimality of pose estimation results. Furthermore, we build a SLAM system that can generate semi-dense or lightweight object-oriented maps with a monocular camera. Extensive experiments are conducted on three publicly available datasets and a real scenario. The results show that our approach significantly outperforms stateof-the-art techniques in accuracy and robustness. The source code is available on https://github.com/yanmin-wu/ EAO-SLAM.
Object-oriented SLAM is a popular technology in autonomous driving and robotics. In this paper, we propose a stereo visual SLAM with a robust quadric landmark representation method. The system consists of four components, including deep learning detection, quadric landmark initialization, object data association and object pose optimization. State-of-the-art quadric-based SLAM algorithms always face observation related problems and are sensitive to observation noise, which limits their application in outdoor scenes. To solve this problem, we propose a quadric initialization method based on the separation of the quadric parameters method, which improves the robustness to observation noise. The sufficient object data association algorithm and object-oriented optimization with multiple cues enables a highly accurate object pose estimation that is robust to local observations. Experimental results show that the proposed system is more robust to observation noise and significantly outperforms current stateof-the-art methods in outdoor environments. In addition, the proposed system demonstrates real-time performance.
Monocular depth estimation is of vital importance in understanding the 3D geometry of a scene. However, inferring the underlying depth is ill-posed and inherently ambiguous. In this study, two improvements to existing approaches are proposed. One is about a clean improved network architecture, for which the authors extend Densely Connected Convolutional Network (DenseNet) to work as end-to-end fully convolutional multi-scale dense networks. The dense upsampling blocks are integrated to improve the output resolution and selected skip connection is incorporated to connect the downsampling and the upsampling paths efficiently. The other is about edge-preserving loss functions, encompassing the reverse Huber loss, depth gradient loss and feature edge loss, which is particularly suited for estimation of fine details and clear boundaries of objects. Experiments on the NYU-Depth-v2 dataset and KITTI dataset show that the proposed model is competitive to the state-of-theart methods, achieving 0.506 and 4.977 performance in terms of root mean squared error respectively.
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